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Knowledge: Document management Building a Smart Laboratory 2015


Knowledge:


Tis chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined- up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re- use of knowledge to develop understanding and to create value. Te objective of a smart laboratory is to


improve the performance of the business and maximise the value of its intellectual capital. Although knowledge management has a strong human component (tacit knowledge), its success is dependent on the preservation of, and access to, explicit knowledge. Traditionally this has been achieved with paper; the digital age presents a new set of challenges.


Scientists have been involved in the development of artificial intelligence (AI) for decades. Te modern version of AI, which sought to create an artificial human brain, was launched in 1956 but had been fermenting many years before that, following the discovery that the brain was an electrical network of neurons that fired in pulses. Over the years, there were apparent breakthroughs


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Document management


that were followed by troughs of despair caused by hardware and soſtware limitations. Among the highlights of this era was a Siri- like machine called ELIZA which, in 1966, could be asked natural language questions and provided voice-appropriate – albeit canned – answers. Te birth of the discipline of knowledge


management (KM) was in the early 1990s. A tidal wave of KM consultants appeared, heralding the birth of this newer AI version and the emergence of supporting hardware and soſtware. A few years later, several scholarly journals appeared as forums for advancing the understanding of the organisational, technical, human, and cognitive issues associated with the creation, capture, transfer, and use of knowledge in organisations. Today the value of tapping into an easily


accessible collection of information, such as a smart laboratory’s assets, is appreciated more than in the past. Te amount of data and information generated by instruments and scientists increases exponentially, and staff turnover is rising to the point where someone with seven years of service with the same employer is now considered an old-timer. Undocumented know-how and locations of information resources are now issues.


Reinventing the wheel is becoming more commonplace – not a desirable occurrence, because the costs of drug development are ever-increasing and fewer blockbuster products are hitting the market. Many soſtware solutions offered to assist


information management are specialised and fragmented. Oſten, disparate divisions of an organisation select their own local soſtware solutions, and IT departments oſten dictate requirements that restrict the scope of possible vendor solutions. Excluding small, single location labs, it is rare to see a smart laboratory where all associated information resides under one roof. Tere are general solutions to support


the processing of large data sets in a distributed computing environment. One of the best known is Hadoop, sponsored by the Apache Soſtware Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes, involving thousands of terabytes. Its distributed file system facilitates rapid data transfer among nodes and allows the system to continue operating, uninterrupted, in case of a node failure. Te Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising.


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