EVENT

Paperless Lab Academy

04 April 2017 to 05 April 2017
Barcelona, Spain

The Paperless Lab Academy is a learning platform, for all organisations involved in running, consolidating, integrating or simplifying laboratory data management processes. 

The Event Structure for 2017 includes a peer to peer networking platform facilitating end-users experiences sharing and discussions, a showcase of various informatics tools and methodologies available today through interactive workshops hosted by leading suppliers and access to all industry attendees at " no fee" charge. In 2017 the central theme is the 2020 roadmap for digital convergence - transforming scientific information into actionable insight. The event will cover digital convergence through a wide range of topics such as harmonisation, integrating IT systems, decreasing financial budgets, increasing privacy and security requirements and the need to reduce overall complexity.

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Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process

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

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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.

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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

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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