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Lab Digitalization Loop Picks Up Speed

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.

Laboratory 4.0: Moving Beyond Digitalization in the Lab

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. In response to this landscape, many laboratories have already implemented digitalization technologies or are in the process of doing so. But with the rise of Industry 4.0, which brings increased automation and digital information transfer in manufacturing, many organizations are contemplating what this means for the future of the lab.

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Fostering Data Standardisation for Collaborative Innovation in the Analytical Lab

R&D-driven industries talk a lot about progressing towards the paperless laboratory. The ultimate aim is to capture and store all experimental, process, inventory and results data, from the earliest stages of discovery, through to manufacturing, QA/QC and even instrument management, in an electronic format

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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