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interview


Smart I


What constitutes a smart laboratory? The answer lies


in a holistic approach, as John Trigg, director of phaseFour Informatics, explains


would say that the real definition of a smart laboratory is not so much about laboratory technologies and scientific disciplines, but the way in which the laboratory operates.


Essentially it’s a blend of the capabilities of the people working in the lab, the quality and effectiveness of the technologies being used, and efficiency and appropriateness of the processes that are in place. Tese three elements are at the heart of what I consider to be a smart lab; an ecosystem embracing people, technology and processes. For some time now we have been


witnessing a convergence between science and technology and our formal education processes are not helping as much as perhaps they could. Formal education traditionally follows discipline-specific lines, but in our laboratories we’re increasingly dealing with the convergence of different types of technologies and scientific disciplines, and this is proving to be a challenge. It’s interesting to note that when you


talk with people who manage laboratory information technologies, you oſten find that they have a scientific background. Taking on this ‘hybrid’ role actually involves a lot of informal ‘on the job’ learning to compensate for the lack of formal education in information technologies. When you start to dig around and look


at what information has been published to address these convergent areas, you find that there’s not a vast amount of reference material available. Tere is some literature covering topics such as LIMS or ELNs, but some of the other systems don’t get addressed at all. Although people may argue that you can attend various training courses to learn what’s necessary for implementing laboratory


12 SCIENTIFIC COMPUTING WORLD


systems, I would draw a distinction between training and education – training tends to focus very much on what to do and how to do it, whereas education is more about developing an understanding of why we need to do things a certain way. It’s about trying to put a perspective around the principles of a smart lab, so that we can fully exploit the capabilities of the technology in the context of the scientific and business objectives of the laboratory. One of the real issues of concern right now


is the fact that a lot of our labs are close to, if not entirely, electronic. ELNs seem to have closed that last loophole in that there is no


THE MORE WE CAN DO TO STREAMLINE OUR LABORATORY SYSTEMS, THE MORE SCIENTISTS WILL BE ABLE TO FOCUS ON WHAT’S REALLY IMPORTANT – THE SCIENCE


real long-term role for paper any more. So we have labs that are essentially electronic, but not necessarily integrated, and this means that they may not be operating to their full potential. Tere are two reasons for this. Firstly there is the fact that over time we accumulate a lot of legacy equipment and systems, and while they may be doing a good enough job, they aren’t utilising the latest technology for connectivity and communication. Secondly, most of the systems in a lab are not necessarily designed


to work together – they may provide a means of exporting data, but are not intended to work closely coupled to other systems. Tis represents a major challenge for a ‘smart lab’; one that will not be easy to overcome. One of the other problems we’ve seen


over a number of years is that scientists oſten end up wrestling with the technology if it is complex, inadequately designed and bureaucratic. Te more we can do to streamline our laboratory systems so that they are less intrusive, the more scientists will be able to focus on what’s really important – the science. Tis year I will be collaborating with


Scientific Computing World on a guide to ‘Building a Smart Laboratory’. My hope is that the guide will really fulfil part of that missing educational side of the laboratory ‘ecosystem’ in the sense that it will not be an instruction manual; it’s about understanding the rationale behind the concepts of a smart laboratory, what the different components are capable of, and helping people make informed choices about the systems and processes they use. Traditionally we take a fairly application-


centric view of solving laboratory problems – for example, in my consultancy role I get approached by people saying that they need an ELN or a LIMS; the initial conversation is about the solution, not about articulating the problem. We don’t always look at the big picture and identify the problem in the context of a business process, taking into account workflows, human factors and longer-term technological considerations. Te Building a Smart Laboratory guide will attempt to address these concerns.


Interview by Beth Sharp www.scientific-computing.com


thinking


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