Curating data into pictures has nuances that are easy to overlook

Modern chemistry increasingly involves exploring large sets of results – which brings the problem of how to display them. I came to know data visualisation through working as a high-throughput experimentation chemist, where I designed experiments to evaluate parameter space (and had to contend with numerous axes showing numerous variables), usually to cajole stubborn reactions into working well. Many other areas of chemistry make great use of visualisation, including complex assays run by discovery chemists and biologists, chemical space portrayals for ligand or drug discovery, or perhaps a map of water contaminants across the country. For larger teams, dashboards showing real-time metrics are gaining in popularity, for automated, at-a-glance information on project completion or instrument maintenance. 

I didn’t initially realise the importance of how I depict data, above other aspects of the report. For some time, my biggest mistake was to present the data without any particular emphasis, in a well-intentioned effort to allow readers to form their own conclusions. A mistake, because I had carefully designed the parameters and laboured over my experiment and dataset for some hours, yet I was expecting readers to be ready to spot the same conclusions in a few minutes.