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