How Do You Manage Small-Scale Lab Experiments with Open-Source Data Analysis Tools?

[davidjames8239]

Hello ACS Community,

I’m running small-scale lab experiments and want to streamline data analysis using open-source tools. What’s your go-to approach for efficient data management?

Details:

• Setup: Grad student, conducting organic chemistry experiments (2025), ~50 samples per run.
• Context: Following ACS resources, I’m using Python-based tools like SciPy and Pandas for data analysis. My current workflow involves manual data entry into CSV files, which is time-consuming and error-prone.
• Steps Tried:
  • Used Pandas for basic statistical analysis and data cleaning.
  • Tried Jupyter notebooks for visualization, but struggled with integrating raw lab data.
  • Explored OpenRefine for data preprocessing, but it’s clunky for small datasets.
• Goal: Automate data entry and analysis to save time and improve accuracy.

Questions:

• Which open-source tools do you use for managing small-scale lab data?
• How do you automate data entry from lab instruments to analysis software?
• Any tips for integrating tools like Pandas or Jupyter into lab workflows?
• For those with efficient setups, what’s your process?

Excited for your insights! Thanks!

[malik887878]

Use tools like Python, R, or Jupyter Notebooks to organize, analyze, and visualize lab data efficiently in small-scale experiments.

[mikewilliam2]

Using Pandas and Jupyter is a solid start you might want to look into tools like LabCollector or ELabFTW for smoother data integration. I’ve had similar workflow issues, and automating inputs made a huge difference. By the way, during downtime, I often check out https://luckpub.com.pk/ a fun little break spot between long data runs.