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Building a Smart Laboratory 2018
Source references from throughout Building a Smart Laboratory 2018
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
Isabel Muñoz-Willery and Roberto Castelnovo, of NL42 Consulting, highlight the importance of developing a robust strategy for the adoption of the paperless laboratory
This chapter looks at how to build a smart laboratory; what approaches to take; and how to deal with potential problems. Inevitably, 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.
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
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
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.
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
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
Robert Roe reports on developments in AI that are helping to shape the future of high performance computing technology at the International Supercomputing Conference
James Reinders is a parallel programming and HPC expert with more than 27 years’ experience working for Intel until his retirement in 2017. In this article Reinders gives his take on the use of roofline estimation as a tool for code optimisation in HPC
Sophia Ktori concludes her two-part series exploring the use of laboratory informatics software in regulated industries.
As storage technology adapts to changing HPC workloads, Robert Roe looks at the technologies that could help to enhance performance and accessibility of
storage in HPC
By using simulation software, road bike manufacturers can deliver higher performance products in less time and at a lower cost than previously achievable, as Keely Portway discovers