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Building a Smart Laboratory 2017 Fig. 1: Information structure


Programmes Document management


Projects


Project management Experiments


Laboratory notebook


Interpreted/processed data SDM/LIMS


Raw data Laboratory instrumentation


focal point for cross-disciplinary collaboration: the point at which the scientific work is collated and traditionally handled by the paper laboratory notebook. Above the experimental layer is a management context that is handled by established groupware and document management tools at the ‘programme’ level, and by standard ‘office’ tools at the project level. Below the experiment level there is an increasing specialisation of data types and tools, typically encompassing laboratory instrumentation and multi-user sample and test management systems. Te triangle also represents the transformation of data to knowledge, the journey from data capture to usable and reusable knowledge that is at the heart of the smart laboratory. Te computerisation of these different


layers has typically happened at a pace driven by available technologies rather than by any coordinated strategy and, as long as experiments are recorded in paper laboratory notebooks, the opportunity to complete the journey from data to knowledge in a seamless way is seriously challenged. Te introduction of electronic laboratory notebooks (ELNs) therefore opens up the possibility of a more strategic approach, which, in theory at least, offers the opportunity for an integrated and ‘smart’ solution. A frequently articulated fear about the


relentless incorporation of technology in scientific processes is the extent to which it can de-humanise laboratory activities and reduce the demand for intellectual input, or indeed, any fundamental knowledge about the science and technology processes that are in use. Te objective of this publication is to present a basic guide to the most common components of a ‘smart laboratory’, to give some general background to the benefits they deliver, and


www.scientific-computing.com/BASL2017


to provide some guidance to how to go about building a smart laboratory. Furthermore, we will look at the rationale and requirements incorporated in laboratory systems in order to comply with broader business requirements. Te two primary areas of technology


that apply to a smart laboratory can be broadly categorised as laboratory automation and laboratory informatics. In general, laboratory automation refers to the use of technology to streamline or substitute manual manipulation of equipment and processes. Te field of laboratory automation comprises many different automated laboratory instruments, devices, soſtware algorithms, and methodologies used to enable, expedite, and increase the efficiency and effectiveness of scientific research in labs. Laboratory informatics generally refers to the


“The two areas of technology that apply to a smart laboratory can be categorised as laboratory automation and laboratory informatics”


application of information technology to the handling of laboratory data and information, and optimising laboratory operations. It encompasses multi-user laboratory systems that provide sample and experiment management, reporting, and scientific data management. In practice, it is difficult to define a


boundary between the two ‘technologies’ but, in the context of this publication, chapter three (Data) will provide an overview of laboratory instrumentation and automation, predominantly data capture.


Data: Instrumentation Chapter four (Information) will look


at the four major multi-user tools that fall into the ‘informatics’ category, identifying their similarities, differences and the relationship between them. Chapters three and four, therefore, focus on the acquisition and management of data and information, whereas chapter five (Knowledge) will provide guidance about the long-term retention and accessibility of laboratory knowledge through online storage and search algorithms that aim to offer additional benefits through the re-use of existing information, the avoidance of repeating work, and enhancing the ability to communicate and collaborate. Te underlying purpose of laboratory


automation and laboratory informatics is to increase productivity, improve data quality, to reduce laboratory process cycle times, and to facilitate laboratory data acquisition and data processing techniques that otherwise would be impossible. Laboratory work is, however, just one step in a broader business process – and therefore, in order to realise full benefit from being ‘smart’, it is essential that the laboratory workflow is consistent with business requirements and is integrated into the business infrastructure in order for the business to achieve timely progress and remain competitive. Chapter seven (Beyond the laboratory) will examine the relationship between laboratory processes and workflows with key business issues such as regulatory compliance and patent evidence creation, and will also address productivity and business efficiency. Te progressive conversion of laboratory


operations to a digital environment has been underway for a few decades, and the concept of an electronic or paperless laboratory is getting closer and closer. Tis type of change has had a dramatic effect on other industries vulnerable to digital technologies. Music, video, newspapers, magazines, and photography are just a few examples of disruptive change that presents new opportunities, but which seriously challenges the status quo. Te situation in laboratories may be less dramatic – mainly because the progression has been incremental – but nevertheless, it is a significant change that is readily recognised by laboratory workers. In some cases it is welcomed; in others it is not. Chapter eight (Practical considerations in specifying and building the smart laboratory) is therefore devoted to the process of making the laboratory ‘smart’, taking into account the functional needs and technology considerations to meet the requirements of the business, and addressing the impact of change on laboratory workers. n


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