![]() ![]() The chemist of the future does not need to be a coder any more than the chemist of today needs to be a glassblower. The most successful organisations are already raising the baseline data literacy of all their staff and focussing on the key skills and software tools that will help their scientists to adapt to this change in the paradigm. The job of the chemist in the future will be less about making samples and more about generating data that can be turned into useful insight. The first step is to recognise the need for better data skills across the scientific workforce. However, this is only valuable as a later step, after the more important groundwork has been done to build the foundations for a data-driven culture. One approach that I have seen work is for organisations to develop a small number of enthusiasts into in-house experts that can code bespoke solutions to streamline data workflows for their colleagues. I talk to scientists from companies big and small from all around the world and nobody has yet cracked it. Yet digital transformation – and developing the skills needed to achieve it – remains one of the biggest challenges that organisations face today. Learning how to build your own digital tools will be a waste of time for most people. Just as with companies that mass produce glassware, software companies have been set up by people that understand the challenges and, working with the scientific community, their dedicated developers have created easy-to-use tools specifically to meet these needs. Most of the tasks that I needed to write code for in the past can now be done in this point-and-click manner and there has been an explosion in the number of commercial no-code or low-code lab automation and data software solutions in recent years. Yet it was a fun way to gain a deeper understanding of SVEM, just as you might gain a greater appreciation of the form and function of flasks and funnels by trying to make your own. I spent a few hours writing some code to do it and the metaphorical broken code bin was soon full. The algorithm involves looping through hundreds of cycles of an analysis routine, which you would not want to do manually. Recently, I was interested in a novel machine learning method called self-validated ensemble modeling (SVEM), which promises to be uniquely useful for analysing the smaller datasets that we typically produce in industrial R&D experiments. I’ve learned enough to do useful things with coding at various times in my career. I know a lot of scientists that love the idea of learning new digital skills, including coding – I’m one of them. Hence, we should send everyone on coding bootcamps. In the lab of the future, routine practical tasks will be automated and researchers will instead spend their time coding those machines and their data workflows. As R&D becomes more digital, there will be less hands-on lab work, the argument goes. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |