The digital laboratory of the future

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There are different types of laboratories: for example, research laboratories that produce innovations that later become products. Another type of laboratory is analytical laboratories, which are important for ongoing orders. The impact of these laboratories on the "health" of a company varies greatly over time: a research laboratory will probably only be able to deliver innovations after years of research. In contrast, the effectiveness of an analytical laboratory is important for the day-to-day success of a company. Even though the efficiency of these laboratories can be measured over different periods of time, the race to design each type of laboratory as the "laboratory of the future" has already begun.

Since work is already underway to redesign laboratories, it could be assumed that in ten years' time, future laboratories will be very different from those of today. The introduction of artificial intelligence (AI) for everyday use - such as Chat GPT - also suggests that similar technologies will soon be able to be used in the laboratory, thus accelerating development.

What will the laboratory of the future look like? To better imagine how iran consumer email list such a laboratory works, we accompany the fictional scientist Alva in her work in the year 2033.

Data-centric and fast access to data and devices
Alva can monitor all the equipment in her lab from her phone or computer at any time. She can access all the data that belongs to her or that her colleagues have shared with her at any time. This data is not just there, but is already structured so that Alva can read it quickly. The software that manages this data is very well designed so that everyone can quickly and easily understand her data. The analysis is also very fast - with the help of software that can be quickly adapted by scientists as desired and needed.

Efficient research through digitalization
Alva and her colleagues can focus mainly on exploratory work. The days when lab staff had to spend a large part of their working time searching for and managing data are over. This data is stored and protected centrally, so scientists have great confidence in the system and do not have to worry about the data being lost. One of Alva's favorite tasks is to work with the data by checking the results or using the software to try to find other parameters that might be important. The organizational work has almost disappeared because the structure in the lab takes care of it thanks to digitalization and automation.