Building a Smart Laboratory 2018

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Summary

In this guide we have attempted to coalesce much of the information required in order to design and implement as smart laboratory or, at the very least, to begin the process of laboratory automation. While it may seem like a challenging prospect, the underlying principles are simple and focused on crafting a strategy that will enable more productivity and insight to be generated from scientific research

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Beyond the laboratory

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

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Information: Laboratory informatics tools

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

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The smart laboratory

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.

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Knowledge: Data analytics

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

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Knowledge: Document management

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

Pages

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Robert Roe explores the role of maintenance in ensuring HPC systems run at optimal performance

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Robert Roe speaks with Dr Maria Girone, Chief Technology Officer at CERN openlab.

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Dr Keren Bergman, Professor of Electrical Engineering at the School of Engineering and Applied Science, Columbia University discusses her keynote on the development of silicon photonics for HPC ahead of her keynote presentation at ISC High Performance 2018 

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Sophia Ktori explores the use of informatics software in the first of two articles covering the use of laboratory informatics software in regulated industries

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Robert Roe discusses the role of the Pistoia Alliance in creating the lab of the future with Pistoia’s Nick Lynch