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Laboratory Informatics Guide 2020


What’s next for the ELN?


Steve Yemm, CEO of BioData, gives his insights on the future of ELN technologies


As technology continues to drive us towards Industry 4.0, characterised by automation, data science and machine learning, every laboratory may have to rethink its digital strategy. Improved integration and workflows, more structured and consistent data logging and new connectivity across the globe are setting new standards in lab productivity. A vital tool in the technology revolution


is the electronic laboratory notebook (ELN), which is well placed to support the pace of change through delivering standardised data logging, faster results retrieval and new collaboration possibilities, all in a secure and compliant platform. Here, Steve Yemm, CEO at BioData explains


how the ELN is helping shape the lab of the future.


Data driven laboratories With more than 70 per cent of researchers polled by Nature admitting to having tried and failed to reproduce another scientist’s experiments, and more than half having failed to reproduce even their own experiments, continually improving ways to log, store, track and reproduce data is critical. Organising data within a defined set of


protocols rather than collecting the scattered, paper-based notes of the past is a clear benefit of using an ELN – and one that paves the way to the future of data analysis and interpretation. Pre-set formatting forces researchers to organise their data in a uniform way, which not only allows for its universal retrieval and analysis, but also lays the groundwork for automated search based on structured data and meaningful data analysis while also allowing the principles of machine learning to be introduced.


Given the wealth of data available, if properly structured, ML can learn to spot trends in the results and add the knowledge to the data bank


“ 4


The ML door is wide open Automating processes delivers significant productivity gains. With labs under increasing pressure to deliver against ambitious throughput targets, both in terms of volume and speed, automating repetitive tasks will deliver significant time – and therefore cost – savings. Collating information in a consistent format


creates order and uniformity in the stored data sets. As well as standardising the process at the front end of data logging, adding in the


principles of structured data to the back end – i.e. effectively assigning metadata to each piece of data to set it in context and facilitate search – opens the door to machine learning. Incorporating artificial intelligence (AI), or


machine learning (ML), into the laboratory is set to make a major impact in the life sciences sector. Te quality and structure of the data and metadata determines the speed of learning and the quality of output.


The potential of machine learning Machine learning can simplify the time required to prepare experiment planning and documentation. Laboratories can spend substantial time attempting to recreate successful experiments. Automated retrieval of the structured data already stored in the ELN can start a draſt report based on past experience and will facilitate recreation of the process as well as meaningful comparison of results. Experiment simulation based on past data will


also be possible, creating significant time savings by predicting likely outcomes, which then allows the scientist to drill down with further research into the areas of most potential. Given the wealth of data available, if properly


structured, ML can learn to spot trends in the results and add the knowledge to the data bank. Tis opens up the exciting possibility of analysing and extrapolating meaning from the data results in order to explore new experimental space that is not available in the wet lab and will allow simulated ‘in silico’ experimentation.


Closer collaboration Global collaboration also reaches new levels via an ELN – particularly for collaboration between different partners, or where outsourcing agreements are in place. It allows different skills sets to be introduced into the research from the specific expertise of different laboratories and scientists, all through a real-time collaboration platform. A biotech lab in Massachusetts, USA, for


example, could outsource a particular chemistry aspect of an experiment to a lab in China, and, similarly, specific biology research to a lab in India, all working from the same live data. New features of an ELN, such as inline editing of documents regardless of their file


www.scientific-computing.com/LIG20


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