Coding can take scientists from participants to owners of their data. More and more it is becoming clear that every organization needs to employ workers with data science skills. As more scientists become data scientists, the move towards digitalized data will accelerate.
Richard Lee discusses the global trend around the development of machine learning (ML) and artificial intelligence (AI) capabilities. As utilization of ML/AI continues to grow, R&D organizations will likely continue to focus on how to normalize data into a format that can be fed into a ML/AI framework.
Andrew Anderson shares his insights about working from home, his non-work coping mechanisms, and how ACD/Labs is building collaborative software interfaces to support continued work from home requirements for analytical chemists.
Andrew Anderson provides his insights on why digitalization and data on the cloud go hand-in-hand in this blog article.
Graham McGibbon shares his vision of what a lab of the future will look like, and how technology and shifting workflows affect the scientist's day-today-routine.