Today’s laboratories are becoming increasingly complex, with ever more data being generated and captured. At the same time, regulatory oversight is stronger than ever and places new compliance burdens on everyday operations.
Join engineering virtual reality pioneers ESI | IC.IDO in this webcast as Eric Kam, Immersive Experience Product Marketing ESI, outlines the differences between 3D stereo CAD data viewing and immersive virtual prototyping.
Robert Stober, Director of Systems Engineering at Bright Computing explains how organizations can benefit from connecting diverse and disparate technologies.
This webcast has been designed to provide a guide to laboratory users who wish to increase their capacity for data transfer and collaboration.
When organisations embark on a voyage to change the way they do business, they set off down a difficult path. There is a risk that users are not engaged, scope spins out of control, delays occur, and changes are not accepted by those whom the change is supposed to benefit.
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
Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory
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.
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
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