The researchers developed a new method to fill in missing data in large datasets without using a complete reference dataset. They broke the data into smaller parts, selected representative portions, and used machine learning to impute missing values. They tested this approach on BMI datasets with over 80% missing values and found that it effectively reconstructed the datasets.