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(2025). НОРМАЛИЗАЦИЯ ШКАЛЫ ИНТЕНСИВНОСТИ УЧЕБНЫХ ЗАНЯТИЙ В АНАЛИТИЧЕСКОМ МЕТОДЕ КОНТРОЛЯ ОБРАЗОВАНИЯ (Version v1) [Data set]. НИЦ Прикладная статистика. https://doi.org/10.5281/zenodo.17374975","publicationIDType":"doi","publicationIDNumber":"https://doi.org/10.5281/zenodo.17588054","publicationRelationType":"References"},"citation:dsDescription":{"citation:dsDescriptionValue":"Dataset Title: Data and Code for: \"Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System\"\n\nDescription:\nThis 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.\n\nContents:\n- Source data: educational grades and GDP statistics\n- AMIS normalization results (3, 5, 9, 17-point models)\n- Comparative analysis with linear normalization\n- Ready-to-use Python code for data processing\n\nApplications:\n- Educational data normalization and analysis\n- Economic indicators comparison\n- Development of unified metric systems\n- Methodology research in data scaling\n\nTechnical info:\nPython code with pandas, numpy, scipy, matplotlib dependencies. Data in Excel format.","citation:dsDescriptionDate":"2025-11-12"},"citation:datasetContact":{"citation:datasetContactName":"Kravtsov, Gennady","citation:datasetContactAffiliation":"Research Center for Applied Statistics","citation:datasetContactEmail":"62abc@mail.ru"},"author":{"citation:authorName":"Kravtsov, Gennady","citation:authorAffiliation":"Research Center for Applied Statistics"},"citation:depositor":"Kravtsov, Gennady","title":"Data and Code for: \"Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System\"","subject":["Computer and Information Science","Social Sciences"],"citation:notesText":"Technical Notes:\n- All Excel files contain original data and AMIS normalization results\n- Python code requires pandas, numpy, scipy, matplotlib libraries\n- Includes examples for educational data (grades) and economic data (GDP)\n- Fixed boundary (2-5) and empirical boundary normalization demonstrated\n- Ready-to-use implementations for 3, 5, 9, 17-point AMIS models\n\nUsage: See 01_README.txt for complete documentation and examples.\n\nТехнические примечания:\n- Все Excel-файлы содержат исходные данные и результаты нормализации AMIS\n- Python-код требует установки библиотек: pandas, numpy, scipy, matplotlib\n- Включены примеры для образовательных данных (оценки) и экономических данных (ВВП)\n- Продемонстрированы нормализация с фиксированными (2-5) и эмпирическими границами\n- Готовые реализации 3, 5, 9, 17-точечных моделей AMIS\n\nИспользование: Полная документация и примеры в файле 01_README.txt.","dateOfDeposit":"2025-11-12","@id":"https://doi.org/10.7910/DVN/BISM0N","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.0","schema:name":"Data and Code for: \"Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System\"","schema:dateModified":"Wed Nov 12 11:15:56 EST 2025","schema:datePublished":"2025-11-12","schema:creativeWorkStatus":"RELEASED","schema:license":"http://creativecommons.org/publicdomain/zero/1.0","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":true},"schema:includedInDataCatalog":"Harvard Dataverse","schema:isPartOf":{"schema:name":"Harvard Dataverse","@id":"https://dataverse.harvard.edu/dataverse/harvard","schema:description":"<span><span><span><h3>Share, archive, and get credit for your data. 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