search.noResults

search.searching

note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
Building a Smart Laboratory 2018


Summary


Summary


In this guide we have attempted to coalesce much of the information required in order to design and implement as smart laboratory or, at the very least, to begin the process of laboratory automation. While it may seem like a challenging prospect, the underlying principles are simple and focused on crafting a strategy that will enable more productivity and insight to be generated from scientific research


T


he five nodes in Figure 9 represent a generalisation of the major knowledge processes, and it is quite evident that technology, in the


form of the laboratory informatics tools, has an enabling role in a laboratory knowledge ecosystem. From laboratory informatics to knowledge


management, technology is predicated on logical and systematic processes. But serendipity has always had a significant role in science. Many scientific advances have originated from ‘what if’ moments, chance observations, and things that went wrong. Failure oſten has more to teach than success!


www.scientific-computing.com/BASL2018


With a growing emphasis on right-first- time, error-reduction, and productivity, it is a management challenge to take the time to review and assess successes and failures. Te role of the informatics tools within a


smart laboratory, or ‘knowledge ecosystem’ (Figure 9) is important. Tey are strong in terms of capturing, recording, and organising data, and increasingly, they provide facilities for sharing information. But further opportunities arise with regard to evaluation


FIG 9


and learning (data analytics) and experimental design. Further afield, they will contribute to predictive science. Nevertheless there needs to be some


space for ‘right brain’ thinking, alongside those systematic and structured approaches for increasing efficiency and productivity. Innovation depends on knowledge and understanding. Although technology can assemble and look aſter the data, making sense of it is down to human assessment


Knowledge processes Sense/explore/discover Evaluate/learn


Intergrate, organise and test with other or pre-existing knowledge (either tacit or explicit) to generate new facts, ideas, knowledge, concepts.


Mostly done as an individual activity –people looking for, or encountering new items of knowledge.


Capture/record


Save the ‘knowledge’ somewhere; e.g. lab notebook, technical report, etc.


Share


Communicate with others (could be explicit or tacit). This forces structure (organisation) and further integration.


Organise


Codify, index, make readily accessible. Could be accomplished or assisted by others, or by automation.


41


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44