Issues

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The imperfect storm

Gemma Church investigates modelling and simulation tools required for accurate weather prediction

Expect Exascale

Robert Roe looks at advances in exascale computing and the impact of AI on HPC development

Learn to fly

Gemma Church looks at modelling and simulation software in the aerospace industry

A look to the future

Robert Roe speaks with laboratory informatics software providers about the future of their software and the introduction of new technologies, such as AI and deep learning

Managing pharma data integrity

Darren Barrington-Light, senior manager product marketing for Thermo Fisher Scientific, explains the importance of integrating a LIMS software package into the pharmaceutical data chain 

Research misconduct

Mark Newton, a consultant from Heartland QA, gives his take on the scale of research misconduct taking place in laboratories

Applying AI

Sophia Ktori completes her two-part series on the use of artificial intelligence in healthcare research

Model Medical Devices

Gemma Church reveals how simulation and modelling are aiding the design and development of a range of medical devices

Looking ahead

Robert Roe looks at technology that could disrupt the HPC ecosystem

Roofline Estimation

James Reinders is a parallel programming and HPC expert with more than 27 years’ experience working for Intel until his retirement in 2017. In this article Reinders gives his take on the use of roofline estimation as a tool for code optimisation in HPC

Simulating the future of cycling

By using simulation software, road bike manufacturers can deliver higher performance products in less time and at a lower cost than previously achievable, as Keely Portway discovers

ISC Show report

Robert Roe reports on developments in AI that are helping to shape the future of high performance computing technology at the International Supercomputing Conference

A standard approach

Sophia Ktori concludes her two-part series exploring the use of laboratory informatics software in regulated industries.

A flash in the pan

As storage technology adapts to changing HPC workloads, Robert Roe looks at the technologies that could help to enhance performance and accessibility of storage in HPC

HPC maintenance

Robert Roe explores the role of maintenance in ensuring HPC systems run at optimal performance

Regulating scientific discovery

Sophia Ktori explores the use of informatics software in the first of two articles covering the use of laboratory informatics software in regulated industries

Fattening up FEA

Gemma Church discusses advances to FEA software as it is now used to simulate a wide range of physical phenomena

Cool runnings

With innovation in cooling technology increasingly more important to ensure energy, performance and cost efficiency of HPC, Keely Portway speaks to experts to find out what is driving the latest innovations

Electronics everywhere

Gemma Church explains the background behind explosive growth in the simulation and modelling of low- and high-frequency electronics

Twenty years of the OpenMP API

Michael Klemm, CEO, and Matthijs van Waveren, marketing coordinator for the OpenMP ARB, along with Jim Cownie, principal engineer, Intel Corporation (UK), consider the development of OpenMP over the last 20 years

Dealing with data

Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory

Data: Instrumentation

This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.

Knowledge: Document management

This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value

Knowledge: Data analytics

This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment

The smart laboratory

This chapter discusses what we mean by a ‘smart laboratory’ and its role in an integrated business. We also look at the development of computerised laboratory data and information management; the relationships between laboratory instruments and automation (data acquisition); laboratory informatics systems (information management); and higher-level enterprise systems and how they align with knowledge management initiatives.

Information: Laboratory informatics tools

This chapter will look at the four major laboratory informatics tools – laboratory information management systems (LIMS), electronic laboratory notebooks (ELNs), laboratory execution systems (LES) and scientific data management systems (SDMS) – their differences and how they relate to each other. Each of these systems functions at or around the ‘Information’ layer (see Figure 1) and typically serves to collate data and information about the laboratory’s operations

Beyond the laboratory

This chapter considers who cares about how smart the laboratory is, and why? It also looks at the broader business requirements and their impact on the laboratory, with an emphasis on productivity and business efficiency, integration with manufacturing and business systems, patent evidence creation, regulatory compliance, and data integrity and authenticity

Practical considerations: Specifying and building the smart laboratory

This chapter looks at how to build a smart laboratory; what approaches to take; and how to deal with potential problems. Inevitably, becoming ‘smart’ takes time, not only due to the level of investment required, but also because of the impact of change and the need to consider legacy requirements.

Summary

In this guide we have attempted to coalesce much of the information required in order to design and implement as smart laboratory or, at the very least, to begin the process of laboratory automation. While it may seem like a challenging prospect, the underlying principles are simple and focused on crafting a strategy that will enable more productivity and insight to be generated from scientific research

References

Source references from throughout Building a Smart Laboratory 2018

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