Building a Smart Laboratory 2017

applications such as Photoshop, Microsoſt Office 365 and Amazon are following these trends rapidly. It is expected that scientific soſtware suppliers will be forced to follow the same model in the years to come. Community collaboration and social networking are changing the value of traditional vendor help desks.

Reduce and simplify workflow complexities Te need to simplify our scientific processes will have a significant impact on reducing data integrity challenges. For example, balance and titrator instruments may store approved and pre-validated methods and industry best practice workflows in their firmware.

Adopt and use industry standards and processes Initiatives such as the Allotrope Foundation are working hard to apply common standards. Te Allotrope Foundation is an international not-for-profit association of biotech and pharmaceutical companies, building a common

“The paper versus paperless discussion is as old as the existence of commercial computers”

laboratory information framework for an interoperable means of generating, storing, retrieving, transmitting, analysing and archiving laboratory data and higher-level business objects.

Finding answers 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

with the transition from paper to digital, which includes both the transfer of paper-based processes to ‘glass’ and the identification and adoption of information and process standards to harmonise data exchange. In its simplest form, an electronic

laboratory notebook (ELN) is the electronic embodiment of a paper laboratory notebook. It is a tool to facilitate the workflows that play out in your particular laboratory. Having said that, laboratory information

management system (LIMS), ELN and lab execution system (LES) applications all support this basic definition as the functionality of these tools has converged. In this guide, we will look into the difference between these applications.

Mobile computing While other industries are implementing modern tools to connect equipment wirelessly, many laboratories still write scientific results on a piece of paper, or re-type them into a computer or tablet. Many modern ELN and LES systems allow electronic connection to a (wireless) network. However, to integrate simple instruments like a pH balance, titration and Karl-Fischer instruments to mobile devices, a simpler approach is required in order to achieve mainstream adoption. Te acceptance of tablets and mobile devices will expand exponentially in the laboratory. Laboratories will need to manage the

challenges presented by new consumers of scientific data outside traditional laboratory operations. Non-invasive, end-to-end strategies will connect science to operational excellence. Technology will be critical, but our ability to change our mind-set to enable this cross-functional collaboration will be the real challenge.

New tools for new users

Traditional mainstream LIMS will face challenges. LIMS has been a brilliant tool to manage predictable, repeatable planned sample, test and study data flows, creating structured data generated by laboratories. In R&D environments, unpredictable

workflows creating massive amounts of unstructured data showed that current LIMS systems lack the capability effectively to manage this throughput. ELNs are great tools to capture and share complex scientific experiments, while an underlying scientific data management system (SDMS) is used to manage large volumes of data seamlessly. More than ever before the tools that are

required must support elements of all the classical laboratory informatics tools in order to provide a comprehensive platform for users. In many cases, todays users need the throughput of a LIMS, the unstructured data capturing capabilities of an ELN and the scalability and throughput that comes from SDMS. To meet these demands many tools are converging around a similar set of capabilities


that can drive laboratory operations forward. Increasingly this means connecting laboratories so that their data can be managed effectively rather than recreating data in different geographical locations. However, connecting a laboratory to its peers for collaboration is just one example – this kind of activity also allows more efficient use of SOPs and workflows – reducing the need for additional work to recreate experiments completed by another lab within the same organisation or partners. Giving laboratory users the opportunity to share ideas with other scientists promotes innovation and new ideas to flourish within a business. Ultimately, laboratory users need to appraise

the tools and functionality available to them and this can only be done by looking at the needs of a laboratory to compare the workflows and processes that they are using and match those with the soſtware that is available. Te lines have begun to blur between he classical models of LIMS and ELN so now consumers must be aware that it is not always the core functionality that is key but the room to adapt and configure workflows along with web or cloud-based deployment that will be key points when choosing the right soſtware. n

Dealing with data

The journey for any laboratory starts with data capture, data processing, and laboratory automation. When samples are being analysed, several types of scientific data are being created. They can be categorised in three different classes. Raw data refers to all data on which

decisions are based and it 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. Tacit knowledge (as opposed to formal or explicit knowledge) is difficult to transfer to another person as we store this information in our brains. In one of the following chapters we will discuss how to maximise creating and sharing knowledge.


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