Building a Smart Laboratory 2018 FIG 1 Information structure


The smart Laboratory Te two primary areas of technology that apply


Programmes Document management


Projects


Project management Experiments


Laboratory notebook


Interpreted/processed data SDM/LIMS


Raw data Laboratory instrumentation


scientific process, and as a means of managing scientific information and knowledge. Laboratory information has traditionally


been managed on paper, typically in the form of the paper laboratory notebook, worksheets and reports. Tis provided a simple and portable means of recording ideas, hypotheses, descriptions of laboratory apparatus and laboratory procedures, results, observations, and conclusions. As such, the lab notebook served as both a scientific and business record. However, the introduction of digital technologies to the laboratory has brought about significant change. From the basic application of computational


power to undertake scientific calculations at unprecedented speeds, to the current situation of extensive and sophisticated laboratory automation, black box measurement devices, and multiuser information management systems, technology is causing glassware and paper notebooks to become increasingly rare in the laboratory landscape. Te evolution of sophisticated lab instrumentation, data and information management systems, and electronic record keeping has brought about a revolution in the process of acquiring and managing laboratory data and information. However, the underlying principles of the scientific method are unchanged, supporting the formulation, testing, and modification of hypotheses by means of systematic observation, measurement, and experimentation. In our context, a smart laboratory seeks to deploy modern tools and technologies to improve the efficiency of the scientific method by providing seamless integration of systems, searchable repositories of data of proven integrity, authenticity and reliability, and the elimination of mindless and unproductive paper-based processes. At the heart of the smart laboratory is a simple


www.scientific-computing.com/BASL2018


model (see Figure 1) that defines the conceptual, multi-layered relationship between data, information, and knowledge. Te triangle represents the different layers of


abstraction that exist in laboratory workflows. Tese are almost always handled by different systems. Te ‘experiment’ level is the 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 introduction of 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 to provide some guidance to how to go about building a smart laboratory.


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 application of information technology to the handling of laboratory data and information, and optimising laboratory operations. 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. 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. 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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