The smart laboratory
Building a Smart Laboratory 2018
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.
The progressive ‘digitisation’ of the laboratory offers an unprecedented opportunity not only to increase laboratory efficiency and productivity, but also to move towards ‘predictive science’, where accumulated explicit knowledge and computer algorithms can be exploited to bring about greater understanding of materials, products, and processes
T
oday the landscape for laboratory technologies is broad and varied. Tis is true purely in terms of the variation of management systems and other
soſtware packages but also due to the proliferation of additional technology such as cloud, mobile technologies and more recently the IoT. Tere is no specific definition of a ‘smart
laboratory’. Te term is oſten used in different contexts to imply either that a laboratory is designed to optimise its physical layout, that it incorporates the latest technology to control the laboratory environment, or that the laboratory is using the latest technology to manage its scientific activities. For the purposes of this publication, it is the latter definition that applies. Using technology to manage scientific
endeavours is conceptually a straightforward task but the subtlety lies in choosing the right combination of technologies that can be adapted to suit the use case of a specific laboratory which may be dictated by geography and personnel as much as it is driven by the availability of technology. As such the right answer to setting up a smart laboratory is not to adopt all possible technological features but to identify which areas of the laboratory need to be accelerated or improved upon. A simple example of this could be found in
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a common problem facing many laboratories – data generated through ‘dumb’ instrumentation such as pH meter or weighing scales. Instruments that are not connected directly to a (Laboratory Informatics Management System) LIMS or Electronic Laboratory Notebook (ELN) type management system present opportunities to introduce error through human data entry but there are multiple ways to solve this problem. One would be to buy new scales for example.
Purchasing a new instrument with smart capabilities could feed that data directly into the LIMS reducing the chance for error. Another approach would be the use of mobile devices which could be used to capture the data at the bench another would be to use a raspberry Pi like device connected to the internet to take the result and feed it into the LIMS. Te choice around whether mobile, IoT or new instruments is one that can only be answered on a case by case basis – there is no one size fits all solution for every laboratory. Te introduction of industrial R&D
laboratories heralded a new era of innovation and development dependent on the skills, knowledge and creativity of individual scientists. Te evolution has continued into the ‘information age’ with a growing dependence on information technology, both as an integral part of the
www.scientific-computing.com/BASL2018
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