LABORATORY INFORMATICS GUIDE 2013 | DATA INTEGRATION ➤
suite and like Thermo Scientific’s product, provides access to all instrument data via a single interface, as well as real-time investigation of results. Again, raw data is converted to an XML storage format for future-proof data archiving and the sharing of data and information without the need to access the original application. Accelrys offers Discoverant, which is a validatable environment that delivers on-demand manufacturing process intelligence by aggregating and contextualising different data types and sources, to create an end-to-end view of the manufacturing process.
CHANGING TECHNOLOGIES Technologies supporting conceptual modelling through ontologies and data standards are maturing rapidly. Although they remain in the early stages of adoption, ontology mediated system interoperability and formal metadata management have substantial potential to facilitate a best of breed or Service-Oriented- Architecture (SOA) strategy. The ontology- based approach will allow the user to integrate existing database sources and achieve interoperability between different data formats and applications. Key to the success of the development of such applications is the need to make existing content in organisational data warehouses or siloed data stores available to ontologies. Both Google and Microsoft are investing here. Balance and titrator vendors are
increasing the value of their instruments by implementing approved and pre-validated methods in their firmware. For example, Sartorius allows methods to be implemented directly in its balances. Mettler-Toledo is deploying LabX middleware to realise that functionality across its balance, titrator and other LabX-supported instruments. This could have an impact on validation efforts in lab and manufacturing operations, such as fewer points of failure during operation, reduced customisation of software, and better documentation. Data-intensive science is becoming far
Data integration facilitates self-documenting processes Planning of analysis
Prepare documentation Prepare analysis Prepare reagents
Document reagent preparation Maintain logbook Create sequence Execute SST Execute run
Document run/values Calculations
Transcribe end result
Close out analysis by lab tech Check run
Laboratory head review Close out
Additional check Copy raw data
Place document in storage 0.0% 2.0% 4.0% Analytical work 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% Documentation Checks
resulted in the need to rethink how data is cost-effectively stored, analysed and shared. Communication is a common dominator. Tacit knowledge is based on common sense, while explicit knowledge is based on academic accomplishment – both are underutilised. Combining explicit information, stored in computer systems, with tacit information is where inventions and knowledge are created and shared. Technology is set to change the dynamics
sense, while explicit knowledge is
accomplishment – both are underutilised’
based on academic
‘Tacit knowledge is based on common
of how scientists work together. The Cloud, for example, is not just an IT initiative; it really changes the ways in which people and science can work together. For example, it eliminates the need to wait for months for a particular scientific paper to be published. Building trust within relationships in order to create these teams remains a people issue and when
considering where active communication occurs in science, thoughts may lean towards scientific presentations and great papers. As John Trigg points out (p.7), Steven
more mainstream. Research is increasingly collaborative and complex, leveraging multiple technologies to get a systems level understanding of diseases and organisms. Data integration is crucial for enabling virtual knowledge sharing and the exponential rise in the scale of (big) data being generated, combined with increased collaboration, has
12 |
www.scientific-computing.com/lig2013
Johnson reported at
TED.com that he had conducted research into where scientific innovation really takes place. What he discovered was that most innovation actually occurred through social interaction at regular face-to-face lab meetings. There, ideas are shared, data challenged, and concepts rallied. Ideas truly become innovation when combined with others or added to existing
facts. Towards the end of the scientific process, conclusions are written as a one-sided conversation with an imaginary colleague, anticipating questions, challenging and stimulating debate. In order to support a strong scientific discussion, people not only need access to relevant facts, but to a collaboration platform which allows virtual real-time interaction with all pertinent information. This forms the basis for capturing the discussion, decision and opinion of an integrated set of people. One example is Shire, a global specialty biopharmaceutical company, which uses IDBS’ E-WorkBook platform to enable virtual laboratory meetings and to streamline and facilitate distributed drug research.
CHEATING IS ALLOWED Those laboratories yet to deploy an informatics system shouldn’t worry – being late in the adoption of lab data integration does have one great advantage. Healthcare, banking and the consumer industry have all adopted paperless and electronic data integration approaches, which means that many accepted technologies are now at the disposal of laboratories. This is an exciting time as cross industry best practices can be used to create start-to-finish knowledge management repositories and enable cross-functional collaboration between internal information silos. l
Peter Boogaard is an independent LIMS consultant:
peterboogaard@industriallabautomation.com
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