We introduce the Guideline-Centered Annotation Methodology (GCAM), a novel data annotation methodology designed to report the annotation guidelines associated with each data sample. Our approach addresses three key limitations of the standard prescriptive annotation methodology by reducing the information loss during annotation and ensuring adherence to guidelines. Furthermore, GCAM enables the efficient reuse of annotated data across multiple tasks. We evaluate GCAM in two ways: (i) through a human annotation study and (ii) an experimental evaluation with several machine learning models. Our results highlight the advantages of GCAM from multiple perspectives, demonstrating its potential to improve annotation quality and error analysis.