You already know the weighing lab of the future revolves around redesigning environments so scientists can reach breakthroughs more efficiently. This means starting the journey to replace slow and error-prone manual processes with automated solutions that free up human resources to focus on true scientific conceptualization. At the center of this process is data.
The data-centric lab of the future will use lab weighing software and automation effectively. However, getting from point A to point B is proving to be a challenge. Many bench scientists still spend a high percentage of their time manually transcribing, importing, and analyzing data. Similarly, data scientists spend up to 70% of their work time finding and curating data before analysis even starts.
More often than not, this scramble for data increases the risk to results accuracy. Invalid or incomplete data from experiments causes a lot of chaos in academia, where time-to-publishing is critical, and manufacturing, where improperly produced articles create dreaded supply-chain issues. Organizational costs in terms of money and reputation can be enormous.
What's more, metadata including experiment context, instrumentation, and provenance should be preserved for the long term. When this metadata fails to be recorded and connected to raw experimental results, valuable insights gained over time can be irretrievably lost.
In short, your weighing lab of the future depends heavily on data-handling excellence. Wherever you are on your journey, our free data management guide will help you crack the code of deciding where and when to start upgrading your laboratory's manual systems and processes.
