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.
Automotive radars are becoming standard equipment on vehicles, with several antenna architectures being used to cover the different safety functions in complex chassis environments and where the side effects become more significant on radar performance.
This whitepaper outlines how the collaboration of organizations across science-based industries and their leveraging of advanced technology will improve lab processes and productivity. Digitalization will accelerate scientific creativity, bringing better products to market faster. Together these trends will not just benefit science but enable better care for patients as well.
With the rapid adoption of cloud computing, learn how Thermo Scientific™ Chromeleon™ CDS supports moving from a traditional on-premise setup to a cloud based deployment and associated benefits and challenges.
21 CFR Part 11 requires Food and Drug Administration (FDA)-regulated industries, including medical device manufacturers, drug makers, biotech companies and biologics developers, to have validation documentation and implement controls such as audit trails and electronic signatures
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