A Data-Driven Perspective on the Colours of Metal-Organic Frameworks

<div> <div> <div> <p>Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale, using chemically meaningful reasoning. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general—that is more suitable for a data-driven approach to chemistry. All data is captured in an electronic lab notebook and subsequently exported to a repository. </p> </div> </div> </div>

A Data-Driven Perspective on the Colours of Metal-Organic Frameworks

<div> <div> <div> <p>Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale, using chemically meaningful reasoning. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general—that is more suitable for a data-driven approach to chemistry. All data is captured in an electronic lab notebook and subsequently exported to a repository. </p> </div> </div> </div>

Helios: History and Anatomy of a Successful In-House Enterprise High-Throughput Screening and Profiling Data Analysis System

We describe the main characteristics of the Novartis Helios data analysis software system (Novartis, Basel, Switzerland) for plate-based screening and profiling assays, which was designed and built about 11 years ago. It has been in productive use for more than 10 years and is one of the important standard software applications running for a large user community at all Novartis Institutes for BioMedical Research sites globally. A high degree of automation is reached by embedding the data analysis capabilities into a software ecosystem that deals with the management of samples, plates, and result data files, including automated data loading. The application provides a series of analytical procedures, ranging from very simple to advanced, which can easily be assembled by users in very flexible ways. This also includes the automatic derivation of a large set of quality control (QC) characteristics at every step. Any of the raw, intermediate, and final results and QC-relevant quantities can be easily explored through linked visualizations. Links to global assay metadata management, data warehouses, and an electronic lab notebook system are in place. Automated transfer of relevant data to data warehouses and electronic lab notebook systems are also implemented.