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Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading, University of Southampton)

Authors: Kanza, Samantha;

Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading, University of Southampton)

Abstract

This video is the eighth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading) Bio: Dr Samantha Kanza is a Senior Enterprise Fellow at the University of Southampton. She completed her MEng in Computer Science at the University of Southampton and then worked for BAE Systems Applied Intelligence for a year before returning to do an iPhD in Web Science (in Computer Science and Chemistry), which focused on Semantic Tagging of Scientific Documents and Electronic Lab Notebooks. She was awarded her PhD in April 2018. Samantha works in the interdisciplinary research area of applying computer science techniques to the scientific domain, specifically through the use of semantic web technologies and artificial intelligence. Her research includes looking at electronic lab notebooks and smart laboratories, to improve the digitization and knowledge management of the scientific record using semantic web technologies; and using IoT devices in the laboratory. She has also worked on a number of interdisciplinary Semantic Web projects in different domains, including agriculture, chemistry and the social sciences. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/PDWZkZBzfqw

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Machine Learning, Artificial Intelligence, ICT, collaborative projects, funding call

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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