Data and Code for: "Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System"

10.7910/DVN/BISM0N Kravtsov, Gennady Gennady Kravtsov Research Center for Applied Statistics Data and Code for: "Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System" Harvard Dataverse 2025 Computer and Information Science Social Sciences data normalization adaptive scaling educational analytics metric integration heterogeneous data statistical distribution Python implementation educational assessment GDP analysis AMIS Kravtsov, Gennady Gennady Kravtsov Research Center for Applied Statistics 2025-11-12 2025-11-12 10.5281/ZENODO.17588054 22434 46238 94610 63141 16577 7824 32411 3750 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet application/vnd.openxmlformats-officedocument.spreadsheetml.sheet application/vnd.openxmlformats-officedocument.spreadsheetml.sheet application/vnd.openxmlformats-officedocument.spreadsheetml.sheet application/vnd.openxmlformats-officedocument.spreadsheetml.sheet text/x-python application/vnd.openxmlformats-officedocument.spreadsheetml.sheet text/plain 1.0 Creative Commons CC0 1.0 Universal Public Domain Dedication. Dataset Title: Data and Code for: "Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System" Description: This dataset contains source data and processing results for validating the Adaptive Multi-Interval Scale (AMIS) normalization method. Includes educational performance data (student grades), economic statistics (World Bank GDP), and Python implementation of the AMIS algorithm with graphical interface. Contents: - Source data: educational grades and GDP statistics - AMIS normalization results (3, 5, 9, 17-point models) - Comparative analysis with linear normalization - Ready-to-use Python code for data processing Applications: - Educational data normalization and analysis - Economic indicators comparison - Development of unified metric systems - Methodology research in data scaling Technical info: Python code with pandas, numpy, scipy, matplotlib dependencies. Data in Excel format. Technical Notes: - All Excel files contain original data and AMIS normalization results - Python code requires pandas, numpy, scipy, matplotlib libraries - Includes examples for educational data (grades) and economic data (GDP) - Fixed boundary (2-5) and empirical boundary normalization demonstrated - Ready-to-use implementations for 3, 5, 9, 17-point AMIS models Usage: See 01_README.txt for complete documentation and examples. Технические примечания: - Все Excel-файлы содержат исходные данные и результаты нормализации AMIS - Python-код требует установки библиотек: pandas, numpy, scipy, matplotlib - Включены примеры для образовательных данных (оценки) и экономических данных (ВВП) - Продемонстрированы нормализация с фиксированными (2-5) и эмпирическими границами - Готовые реализации 3, 5, 9, 17-точечных моделей AMIS Использование: Полная документация и примеры в файле 01_README.txt.