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Bridging the language divide

Tablets and smartphones have revolutionised the way in which we all work, and changed expectations of how everyone should be able to work. Modern workers need to communicate and share information, from any location, and from any mobile platform. Making potentially sensitive analytical and other scientific data available to the right personnel, in real time and in a suitable format, is no different, conceptually, at least. Yet in an R&D or life-science testing environment, data is likely to comprise multiple layers of information, coming out of multiple instruments from multiple vendors, which may all use different native software.

Interpreting data formats

BSSN Software recently launched its Seahorse Mobile Edition platform as a vendor-independent solution for delivering scientific data to mobile devices, supporting a wide range of analytical data types, from HPLC and mass spectrometry, to medical imaging, microplate readers and bioreactors. The platform effectively sits alongside the data source – for example, a scientific data management system (SDMS), which provides an interface with existing data repositories, and interprets the data format. ‘In today’s world of mobile working, it is really important for people to be able to access their analytical data wherever they are, perhaps to see what analytical results have been generated overnight, while they are on the train to work in the morning’, commented Burkhard Schaefer, BSSN president and lead architect of the AnIML data standard on which BSSN’s Seahorse family of software is built. ‘Users may want to flick through data from previous experiments to help decide on their next round of analyses, and they may not necessarily be in the laboratory or at a workstation when they need to be able to do this.’

Improving compatibility

Seahorse Mobile Edition is the latest development in BSSN’s drive to provide software that will get the right data, in usable and ultimately reusable formats, to the end users – the people who need to review and potentially make decisions based on that data. The firm, based in Darmstadt, Germany, is also developing tools that will facilitate the integration of experimental and analytical results in formats that are compatible with other R&D platforms and systems, such as an electronic laboratory notebook (ELN) or laboratory information system (LIS), for example, Schaefer explains.

‘It’s very easy to focus on what the latest state-of-the-art instrumentation and equipment is capable of, and then forget that we also need to be able to use and reuse the analytical data coming out of that instrumentation. A vast amount of data ends up in silos or locked in proprietary instrument software. It can be difficult to mine, interrogate, or share across organisational boundaries, and a vast amount of the value of that data is then lost. In the same way, we need to make sure that we can integrate all this experimental and analytical data with information management and other software systems.’

Making data integration smarter

A key point here is to make data integration smarter, so that only relevant information is passed into other layers of informatics systems, Schaefer adds.

To enable smarter data integration, BSSN has developed a mapping framework that makes it possible to fine-tune the flow of data from analytical instrumentation into data management and other informatics platforms. ‘For example, you will want to pass all your chromatogram and peak information into an ELN, but you may only need an analyte concentration measurement to be fed back into a LIMS, so that a decision can be made on whether to release a batch. Our mapping framework allows you to define those links and specify which packets of information need to go into any target data system.’

This need to optimise data accessibility, usability, and integration has been built into BSSN’s development of its Seahorse family of software solutions for capturing, viewing, and sharing analytical and biological data. The product portfolio is founded on the firm’s flagship Seahorse Scientific Workbench, a vendor-neutral software suite that can capture and manage raw analytical and results data from a wide range of experimental techniques and instrumentation. Seahorse Workbench allows disparate data types to be interpreted and viewed side-by-side, irrespective of the original software, with support from visualisation, annotation and reporting features.

Pioneering the AnIML data standard

BSSN is now testing a beta version of a Seahorse Web Edition, which will ultimately provide similar functionality to the desktop edition, including data analysis and reporting. For the bioprocess sectors, BSSN offers its Bioprocess Manager suite, which supports the complete bioprocess development and execution workflow. Contract Research Manager, also based on the Seahorse Workbench, has been developed to facilitate the streamlining of communication, ordering analytical results and workflow data-sharing and interaction, between client and CRO. Key to all BSSN’s platforms are the vendor-neutral mechanisms of data capture, built on the XML-based AnIML data standard, which has been developed for the storing and sharing of multiple experimental data formats, for a wide range of scientific disciplines. BSSN is pioneering AnIML, and offers a number of converters that can transpose data from other formats directly into AnIML.

‘What’s particularly interesting to us, is that whereas two years ago we were working almost exclusively with end users, today, half of our business is with the instrument and software vendors, who are putting our technology into their own products to facilitate integration,’ Schaefer comments. ‘We can provide insight into the everyday workflows that people are doing. What matters in the end is a quicker time to market, which can be achieved by leveraging our framework.’

Collaboration with SiLA

As part of its role in pioneering the AnIML data standard, BSSN Software has been involved in what Schaefer maintains could be a ground-breaking collaboration with the non-profit SiLA consortium for Standardisation in Lab Automation. SiLA is spearheading the development and introduction of interface and data management standards that will facilitate rapid integration of lab automation systems, Schaefer explained. ‘SiLA is focusing on developing a standard for the communication side, where AnIML is the data side. The potential for synergy is evident, and SiLA and AnIML have been working together for the last year. At the recent MipTec exhibition in Basel, we worked with SiLA to demonstrate the power of standardised communication. For the demonstration, at the SiLA stand, users would simply put a piece of candy on a Mettler Toledo analytical balance. They could then go into Seahorse Scientific Workbench and initiate a workflow that required that candy to be weighed. The request was made simply using the SILA standard communication protocol – which is also XML-based – and the instrument made the measurement and sent the data back as a standard AnIML file. It was a real eye opener as to how, if standards bodies work together, so much more can be achieved. That’s a prospect that is really exciting.’

Doing more with your data

In parallel with its drive to promote the standardisation of informatics communication, and having developed the tools for collating disparate data together and effectively publishing it in standard and usable formats, BSSN is also turning its attention to the development of new solutions that will add value to existing informatics layers by allowing scientists to do even more with their data. ‘Data aggregation was the first step, and now we need to see what kind of data processing and workflow management we can put on top of that, so that we can cover an entire workflow within an organisation, all based on open standards,’ Schaefer explained. ‘This will involve the development of domain-specific tools that will allow scientists to carry out data post-processing, independently from that instruments software.

‘The aim is to enable users to pull data together from different instruments and not just aggregate it, but act on it – for example, to request additional analytical tests on relevant instrumentation, using a single tool, and without having to revert to that instrument’s own software. When you can do all that, then you have really got something sustainable that can cover an entire workflow within an organisation with full reproducibility.’


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