Data governance is an integral part of a regulated company’s quality system. Having a chromatography data system can simplify system administration and ensure regulatory compliance (including 21 CFR Part 11) and adherence to data integrity guidelines.
Audit trails are considered the key to the security of a system since they track changes to data and metadata. In this way, an incomplete or absent audit trail can impact data integrity or even product quality. The absence of an audit trail is considered to be, “highly significant when there are data discrepancies” according to the FDA.
Today, laboratory-based organizations face a wide variety of unaddressed data management challenges, and yet ultimately the scientific data is the currency with which they trade. Proper data management may not pay shareholders but it fundamentally defines the integrity of the organization and it’s purpose for existing. Being the cheapest, the fastest or the most definitive is desirable but it is all meaningless if the data is untrustworthy.
Externalization of R&D activities and the deluge of instrumental analytical data generated on a daily basis has resulted in increasing interest in analytical data standardization. Any standardization efforts, however, to either a single format or for data exchange between formats; should be weighed against the requirements of different users of that data, and hardware innovations.
The benefit of design exploration and optimization is understood and accepted by engineers but the required intensive computational resources have been a challenge for their adoption into the design process. The HyperWorks Unlimited (HWUL) appliance provides an effective solution to these challenges as it seamlessly connects all the necessary tools together in the cloud. The aim of this study is to showcase the benefits of HWUL on an optimization driven design of a complex system. For this purpose, an automotive seat design for crash loadcases is selected as an example.
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
Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory
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.
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 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