Robert Roe looks at the use of precision medicine and its potential impact on laboratory informatics software
Data types used are advancing from the simple text formats of old, writes Paul Denny-Gouldson
Gemma Church looks at the use of additive manufacturing processes in engineering
The failure of a candidate drug can cost millions – so many chemists are turning to software that provides modelling capabilities and multi-parameter optimisation
Robert Roe interviews laboratory informatics software providers who discuss potentially disruptive technologies and their impact on the laboratory informatics market
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 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 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
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 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.
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.