Waters Technologies Ireland Limited

Simon Meffan-Main

Richard Chapman

Phillip Young

Tracy Hibbs

Exemplary embodiments provide methods, mediums, and systems for visualization and advanced data science on information collected in an analytical data system. Embodiments identify correlations and patterns in chromatography metadata around areas of potential user error. Correlations between these data sources may point to compliance risk areas. Metadata from the analytical system may be combined with other data sources and/or analytical data to correlate an analytical outcome with compliance artifacts. Supervised and/or unsupervised machine learning techniques may be used to combine these data source and learn correlations between them and compliance risks. The results of these analyses may be displayed on a dashboard, allowing a user to visualize compliance risks across an entire enterprise or supply chain. Automatic notifications of compliance risks may be generated and presented on a user interface. A system may also use pattern recognition to provide insights around potential compliance risks that have not yet occurred.

Methods, mediums, and systems for linking chromatography data and metadata to compliance risks

Techniques and apparatus for ion source devices with minimized post-column volumes are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to receive analytical information from at least one analytical instrument, and generate at least one record in a distributed ledger having a transaction associated with at least a portion of the analytical information. Other embodiments are described.

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Manchester, United Kingdom

Simon Meffan-Main