A roundup of software tools available to scientists using HPC and AI software
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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
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
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.
However, the choice of best-of-breed laboratory instruments and instrument systems can present challenges when it comes to getting everything to work together in a seamless way. The final part of this chapter will look at the issue of standard data interchange formats, the extent of the challenge, and some of the initiatives to address them
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.
Informatics experts share their experiences on the implementation of new technologies and managing change in the modern laboratory
Isabel Muñoz-Willery and Roberto Castelnovo, of NL42 Consulting, highlight the importance of developing a robust strategy for the adoption paperless laboratory operations
This chapter serves as an introduction to this guide, Building a Smart Laboratory 2019. We hope to highlight the importance of adopting smart laboratory technology but also to guide users through some of the challenges and pitfalls when designing and implementing paperless technologies in the laboratory.
As competition in the processor market heats up there are increasing options available to HPC users, writes Robert Roe
Robert Roe looks at the development of exascale in Europe, funded through the European Commission, its member states and industry partners
An international team of scientists that includes researchers at the Lawrence Berkeley National Laboratory and the Karlsruhe Institute of Technology (KIT), have announced a breakthrough in research which aims to measure the mass of the neutrino
Harry Richardson highlights the value of Ceph in scientific applications, in this guest article from SoftIron
Robert Roe explores the use of HPC storage technology in off-shore energy exploration
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Scientists and researchers are using AI to help accelerate the discovery of new drugs for a wide variety of different medical applications.
Integrators and cloud providers help facilitate access to HPC and deliver additional expertise and support, which helps scientists to effectively use computing resources, finds Robert Roe
Today’s DNA sequencing technologies now make it possible to sequence whole human genomes cost effectively and with speed.
The world of MultiBody Dynamics simulation is changing, writes Gemma Church
Quantum technology is going through a period of rapid development, with several technologies driving the adoption of this emerging computing framework, finds Robert Roe
A roundup of cloud technology providers that support researchers using HPC