Technical issues Building a Smart Laboratory 2012


in fixed lab environments. However, they offer an effective solution where there is a specific need for mobility. Te increased market penetration of smartphones and tablets in the consumer market has led to a rising interest in their potential in a business context through the deployment of dedicated apps or browser-based access. In general, small format handheld devices offer good access to data and information, but are typically limited when it comes to data input.


Cloud


Concurrent with this interest in mobile devices is the deployment of cloud-based infrastructures that provide hosted services. Tis approach brings with it opportunities to deploy rapidly at low cost with little or no capital expenditure, but does raise some questions about security, data integrity and data ownership. Some informatics vendors already offer


this type of service. Cloud services generally fall into one of two categories: public clouds and private clouds. Public clouds utilise a single code base for the service to multiple clients that limits customisation and integration, but helps keep costs down. A private cloud will typically offer a code base specific to an individual client and will accommodate customisation and integration, but will normally come at a higher management cost. Interest and uptake of cloud solutions for


informatics systems is somewhat constrained by IP, legal, regulatory and security concerns. Additionally, at the time of writing, cloud computing is suffering considerable hype – there is little doubt that in some markets the


SaaS vs. cloud


Cloud: All


about the data


• Service level agreement


• Emergency and escalation plan


• Back-up services • On-demand scalability • On-demand capacity


SaaS: All


about what


you can do with the data


• Subscription (Pay as you go) • Free and paid services


• Application consultancy offerings


• Application support • Automatic updates • No investment in software • Minimal hardware investment


26 SaaS and cloud candidates


Prototyping which requires lots of IT infrastructure Mobile applications Non-core applications Intensive collaboration projects When scalability results in high down time


Applications requiring high pre-project investments


Projects not requiring strict 21CFR compliance Big data projects


benefits of the cloud outweigh the risks, but in established laboratory environments, the case is unproven. Cloud computing is a combination of


technologies and service offerings that has the potential to increase the speed of basic research projects significantly. Te effort to build such a high-performance computing infrastructure has been significantly changed from weeks to minutes and doesn’t require traditional IT staffing anymore, since it is all offered as an external service.


Pay by the cycle


Te cloud as an infrastructure gives researchers computational access on a subscription or pay-by-demand cost structure. Instant access to computational power, significant lower administration costs, no capital spending headaches and no dependence of availability of IT resources. Soſtware as a service (SaaS) is a soſtware delivery model in which soſtware and its associated data are hosted centrally in the cloud and are typically accessed using a thin client computer or tablet device, or using a web browser over the internet. Zero footprint applications imply that no


soſtware needs to be pre-installed on your client. Tis significantly helps to simplify installation procedures – a browser is all you need. To successfully upload large datasets, it is critical to have access to fast network infrastructures. Limited network bandwidth, especially in start-up phase, may result into frustration and should be avoided. As a researcher, you want to avoid thinking in computer terms. Te table on page 27 summarises the


overall nomenclature and major acronyms for the most common service models.


Operational vs. investment budgeting


From a financial perspective, the model is attractive. Instead of spending significant capital and subsidising a lengthy budget and planning process, a system can now be funded on an expenses budget, using a pay- as-you-go pricing model, which can mean that you stop paying for equipment that sits idle between experiments. Te cloud will significantly increase the speed of executing big data computing in research because there will be almost no wait for approving costly IT budgets. Tere is also no need to invest in perpetual and expensive soſtware licenses. Te cloud has the potential to be the best new development going mainstream for scientific researchers – access to an almost unlimited amount of computer power to achieve scientific calculations and text searches, combined with an almost unlimited size of disk space for an affordable price.


Data storage


Most of the short-term benefits of deploying an informatics system are associated with personal and laboratory productivity; however, the long-term benefits may accrue from the accumulated content of the system’s information/knowledge repositories. Tis may raise further IT considerations over time with regard to how this information is managed and used. Tis means that the provision of adequate data storage space must be taken into account. As the volumes of data grow, there is likely to be an increasing need for better search and visualisation technologies than are typically available today. In addition, consideration must be given


to the nature of the data and how it can be efficiently stored, retrieved and interpreted. For these purposes, it is necessary to distinguish between the content of the ELN (experimental write-up) and external data (laboratory data) to which the write-up may be electronically linked. Over time, they may present two separate data preservation problems. Lab data is oſten stored in proprietary formats, so forward compatibility to future application and operating system releases will be critical. Additionally, the location of these files will need to be managed carefully to avoid breaking electronic links. Electronic records management is


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