Sridar Chittur

For a large production lab, nonideal samples are a no no but we don't have the luxury to refuse. Academic core spend a lot of time and intellectual investment to troubleshoot and suggest improvements. This is especially important in the age of open data science.

13.12.2024 22:21 — 👍 3    🔁 0    💬 1    📌 0

Well said, Cathy. Only when we tinker with hard samples with many limitations do methods get developed so that you can even conceive going to production mode.

13.12.2024 22:20 — 👍 3    🔁 0    💬 1    📌 0