Interviews

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25 August 2015

Continuing her company profiles, Sophia Ktori finds that Waters NuGenesis is differentiated because it can capture data from any instrument in the laboratory

07 July 2015

Continuing her series profiling companies providing informatics software, Sophia Ktori looks at the innovative technology of Core Informatics

07 July 2015

Sophia Ktori profiles IDBS, a company focusing on analytics as well as informatics

24 April 2015

Lonza Biosciences MODA system offers not just a paperless but a mobile and sanitisable way to carry out microbiology testing. Sophia Ktori reports

24 April 2015

Autoscribes LIMS has more than the usual applications, from lotteries to vehicle rentals, as Sophia Ktori discovers

10 February 2015

What happens when a relatively small informatics company is acquired by a very large healthcare corporation? Tom Wilkie looks at the case of Abbott Informatics and Starlims

Pages

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Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process

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Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory

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This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.

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This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value

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This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment