An introduction to: Building a Smart Laboratory 2018
Building a Smart Laboratory 2018
AN INTRODUCTION TO Building a Smart Laboratory 2018
It’s rare for a company to start with a clean slate when making decisions about laboratory automation
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his chapter serves as an introduction to this guide Building a Smart Laboratory 2018. We hope to highlight the importance of adopting smart
laboratory technology but also to guide users through some of the challenges and pitfalls when designing and running the latest technologies in the lab. For any laboratory a cost/benefit analysis
needs to consider the functionality already provided by legacy applications – as well as business justifications. Tis guide will help you understand what informatics processes are needed in laboratories, and why the laboratory should not merely be seen as a necessary cost centre. Only by becoming smart – as this guide
outlines – can lab managers change that mind-set and generate true value for their organisation. Many laboratory operations are still
predominantly paper-based. Even with the enormous potential to reduce data integrity for compliance, to make global efficiency gains in manufacturing and to increase knowledge
sharing, the barriers to implementing successful electronic integrated processes oſten remain a bridge too far.
The informatics journey
Te journey starts with data capture, data processing, and laboratory automation. When samples are being analysed, several types of scientific data are being created. Tey can be categorised in three different classes. Raw data refers to all data on which decisions
are based. Raw data is created in real-time from an instrument or in real-time from a sensor device. Metadata is ‘data about the data’ and it is used
for cataloguing, describing, and tagging data resources. It adds basic information, knowledge, and meaning. Metadata helps organise electronic resources, provide digital identification, and helps support archiving and preservation of the resource. Secondary or processed data describes how
raw data is transformed by using scientific methodologies to create results. To maintain data integrity, altering methods to reprocess will require a secured audit trail functionality, data and access security. If metadata is not captured, the ability to find and re-use previous knowledge from scientific experiments is eliminated.
Paperless or less paper?
Data-intensive science is becoming far more mainstream; however, going digital in the laboratory has been a relatively slow process. More than 75 per cent of laboratory analysis starts with a manual process such as weighing; the majority of results of these measurements are still written down or re-typed. Tere are exceptions: probably the best
example of integrated laboratory automation can be found in how chromatography data handling systems (CDS) operate in modern laboratories. Te characteristics of such a system include repeatable, oſten standardised, automated processes that create a significant amount of raw and processed data. Te paper versus paperless discussion is as
old as the existence of commercial computers. In the 1970s, just aſter the introduction of the first personal computer, Scelbi (Scientific, Electronic and Biological), Business Week predicted that computer records would soon completely replace paper. It took at least 35 years before paperless operations were accepted and successfully adopted in many work operations. Although they have been accepted in banking, airlines, healthcare, and retail, they lag behind in science. Te journey from paper to electronic begins
www.scientific-computing.com/BASL2018
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