g This is an essential part of the

integration strategy, Pedersen said. ‘It enables better sharing of data more widely. Look at how fast the scientific communities have been working to develop new understanding of, and vaccines for, Covid-19. That is partly thanks to the ability to integrate systems and contextualise data and results, but also due to sharing data in a collaborative way to make integration comprehensive and meaningful.’ A key factor in being able to quickly

derive meaningful insights is the ability to aggregate data and standardise data, taxonomies and ontologies, Pedersen reiterated. ‘There’s a great move towards building in a more semantic data layer that makes it possible to ask better questions and get more intelligent answers from the data.’

Maintaining intelligence A key challenge for scientists, Pedersen suggests, is to maintain intelligence, without dilution from internal and external data sources. So the age-old issue of toolkit integration may not necessarily be the biggest problem, he believes. The Certara D360 scientific informatics

platform has been developed to allow organisations to do just that, and collate and analyse complex chemical, biological, logistical and computational data from a wide variety of sources, more cohesively, so that the maximum amount

20 Scientific Computing World Winter 2021

“Software vendors are trying to provide their customers with an environment that facilitates decision- making based on insight from many data sources”

of intelligence can be derived from that data, Pedersen explained. ‘Importantly, D360 is an application that sits atop of all these assay, analytical and experimental systems that generate the data, so that scientists across an organisation can view, understand and leverage that data, to drive the right outcomes. So, even for a large biopharmaceutical company that may have labs carrying out analytical or research-based workflows at very different levels, including in vitro and in vivo work, D360 ensures that the data from all these sources is accessible, and can be viewed and analysed in context.’

Data complexity and volume Data complexity and volume are impacting hugely on lab integration complexity, suggested Robert D Brown, vice president product marketing at Dotmatics. ‘Think about how science is racing ahead. Ultimately it falls to the Research IT organisation to devise,

develop, or acquire the software that can handle that breadth, detail and volume of data, so the scientists can make sense of it.’ From a vendor’s point of view, this

complexity means that a typical lab will now have multiple software and hardware platforms for sample and inventory management and registration, along with potentially multiple ELNs for different parts of the overall lab function and infrastructure, Brown noted. ‘The end users are, effectively, creating

the integration problem, by pushing the boundaries of science. Research IT departments, in combination with vendors such as ourselves, are working to solve that problem. In the real world these IT groups give us a tremendous amount of input. This helps us to develop the optimum solution, initially to meet the needs of perhaps individual clients, but which we can then take out more broadly to industry.’

Industry needs as a driving force In fact, most vendors would likely say that their software is driven by the needs of the industry, he continued. ‘But at Dotmatics this is truly the case: each innovation here could probably be traced back to the needs of one or two customers, who then helped to define the end product. The ultimate aim is to develop a solution that solves the initial problem, but that can be easily configured

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