Data collection in nanotechnology involves a variety of experimental techniques such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and spectroscopy. The data from these techniques can be highly detailed but also noisy and complex. Data preparation involves cleaning, normalizing, and sometimes augmenting the data to make it suitable for training supervised learning models. Techniques like data normalization and feature extraction are critical in making the dataset more manageable and meaningful for the learning algorithm.
