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!
