DataChaperone has been awarded an Netherlands Enterprise Agency subsidy to explore ambitious automation in the life sciences.
Every manual step in a lab process introduces risk. Errors, bias, and inconsistencies make it harder to control quality — and nearly impossible to scale.
With this project, we’re pushing towards a future where experiments are:
– more reliable thanks to automated monitoring and error handling
– more reproducible through fully traceable workflows
– ready for the next step — whether that’s AI-driven insight discovery or fitting seamlessly into your lab’s data and growth ambitions
This is how we see the path to truly scalable life science research!
Why flow cytometry gating is a machine learning problem worth solving
Manual gating introduces variability, requires second-person review, and becomes a bottleneck as data volumes grow. This blog explains why gating is fundamentally a machine learning problem and how it can be standardized and automated in practice.
