Adaptive Multi-Interval Scale (AMIS): Open-Source Software, Source Data and Results for Normalizing and Comparing Raw & Aggregated Metrics

This dataset contains the complete resources for applying the Adaptive Multi-Interval Scale (AMIS) method to normalize and compare heterogeneous data. The AMIS method addresses the fundamental problem of integrating indicators measured in fundamentally incomparable scales. It is specifically designed to work with both raw individual measurements and aggregated data (frequency distributions, summary tables). The dataset provides open-source Python software that implements the full AMIS pipeline: from data loading and transformation to adaptive normalization and the creation of matching tables. It includes tested algorithms for converting aggregated data into a normalized form and a robust inverse transformation mechanism. The dataset ensures full reproducibility of the research and provides a practical toolkit for interdisciplinary data integration and analysis.