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
Today the landscape for laboratory technologies is broad and varied. This is true purely in terms of the variation of management systems and other software packages but also due to the proliferation of additional technology such as cloud, mobile technologies and more recently the IoT.
There is no specific definition of a ‘smart laboratory’. The term is often 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 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. The 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.
The introduction of industrial R&D laboratories heralded a new era of innovation and development dependent on the skills, knowledge and creativity of individual scientists. The evolution has continued into the ‘information age’ with a growing dependence on information technology, both as an integral part of the 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. This 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. The 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 model (see Figure 1) that defines the conceptual, multi-layered relationship between data, information, and knowledge.
Figure 1
The triangle represents the different layers of abstraction that exist in laboratory workflows. These are almost always handled by different systems. The ‘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. The 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.
The 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. The 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.
The 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. The field of laboratory automation comprises many different automated laboratory instruments, devices, software 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, Data: Instrumentation will provide an overview of laboratory instrumentation and automation, predominantly data capture.
Information: Laboratory informatics tools will look at the four major multi-user tools that fall into the ‘informatics’ category, identifying their similarities, differences and the relationship between them. These two features, therefore, focus on the acquisition and management of data and information, whereas Knowledge: Document management 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.
The 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.
Further afield: 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.
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.
