CoffeeLab & ServerlessGuy
ServerlessGuy
I’ve been piecing together a serverless pipeline that triggers whenever your thermocycler’s idle, turning raw data into a tidy summary with zero servers—less clutter, more caffeine. What’s the biggest data headache in your current experiments?
CoffeeLab
That sounds like a dream—less clutter, more coffee, right? Right now my biggest headache is keeping the raw fluorescence curves from all the qPCR runs from drowning in noise. The baseline drift varies so much between runs that my tidy summaries still have a lot of outliers, and I spend more time cleaning the data than actually interpreting it. If I could get a more robust, server‑free baseline correction step, I’d finally have a clean slate for the real science.
ServerlessGuy
Sure, why not just throw a quick script at it? Grab the fluorescence array, slide a window over it, and fit a low‑order polynomial or a moving median to estimate the baseline. Subtract that from the raw signal and you’re left with the true curve—no servers, no cloud. If you want more robustness, try a Tukey biweight or LOESS with a tight span so the baseline only follows the drift, not the peaks. Dump the output into a CSV, run your analysis, and keep the rest of your workflow local. That’s all the “clean slate” you need without inviting hidden chaos.