Features

12 February 2020

For the potential for AI and machine learning methods in drug development to be realised, companies and organisations are forming partnerships to better understand and develop these technologies, writes 
Sophia Ktori

11 February 2020

This chapter looks at how to build a smart laboratory; what approaches to take; and how to deal with potential problems. Becoming ‘smart’ takes time, not only due to the level of investment required, but also because of the impact of change and the need to consider legacy requirements.

 

11 February 2020

This chapter considers who cares about how smart the laboratory is, and why? It also looks at the broader business requirements and their impact on the laboratory, with an emphasis on productivity and business efficiency, integration with manufacturing and business systems, patent evidence creation, regulatory compliance, and data integrity and authenticity

11 February 2020

This chapter will look at the four major laboratory informatics tools – laboratory information management systems (LIMS), electronic laboratory notebooks (ELNs), laboratory execution systems (LES) and scientific data management systems (SDMS) – their differences and how they relate to each other. Each of these systems functions at or around the ‘Information’ layer (see Figure 1) and typically serves to collate data and information about the laboratory’s operations

11 February 2020

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

11 February 2020

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. 

However, the choice of best-of-breed laboratory instruments and instrument systems can present challenges when it comes to getting everything to work together in a seamless way. The final part of this chapter will look at the issue of standard data interchange formats, the extent of the challenge, and some of the initiatives to address them

 

07 February 2020

This chapter discusses what we mean by a ‘smart laboratory’ and its role in an integrated business. We also look at the development of computerised laboratory data and information management; the relationships between laboratory instruments and automation (data acquisition); laboratory informatics systems (information management); and higher-level enterprise systems and how they align with knowledge management initiatives.

07 February 2020

Informatics experts share their experiences on the implementation of new technologies and managing change in the modern laboratory

07 February 2020

Isabel Muñoz-Willery and Roberto Castelnovo, of NL42 Consulting, highlight the importance of developing a robust strategy for the adoption paperless laboratory operations

07 February 2020

This chapter serves as an introduction to this guide, Building a Smart Laboratory 2019. We hope to highlight the importance of adopting smart laboratory technology but also to guide users through some of the challenges and pitfalls when designing and implementing paperless technologies in the laboratory.

05 February 2020

As competition in the processor market heats up there are increasing options available to HPC users, writes Robert Roe

05 February 2020

Robert Roe looks at the development of exascale in Europe, funded through the European Commission, its member states and industry partners

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