High-Dimensional Scientific Data Normalization Pipeline

Create a robust data normalization framework capable of handling high-dimensional scientific datasets with complex, non-linear measurement distributions. Develop a modular Python script that can automatically detect and correct for batch effects, instrument drift, and inter-experiment variability. The solution should include automated feature selection, multiple normalization techniques (z-score, min-max, robust scaling), and generate comprehensive diagnostic visualizations.