cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18758545
title: Data Set Artificial Intelligence and Multicultural Religious Education: A Systematic Review of Ethical Mediation and Decolonial Practices

description: This dataset comprises the complete supplementary materials for a decolonial qualitative systematic review examining how Artificial Intelligence (AI) mediates Multicultural Religious Education (MRE) across Islamic, Christian, Jewish, Hindu, Buddhist, and indigenous educational traditions. The review synthesizes 254 peer-reviewed studies (2014–2026) from English, Indonesian, and Arabic sources, following PRISMA 2020 guidelines.

The core contribution is an empirically-grounded framework identifying six AI mediation mechanisms along a continuum from algorithmic substitution (moral outsourcing, normative amplification, cultural flattening) to dialogic integration (hybrid deliberative mediation, contextual value re-embedding, dialogic ethical framing). The analysis further documents five decolonial possibilities emerging from Global South practices: epistemic re-centering, pedagogical sovereignty, cultural counter-flattening, governance innovation, and transnational solidarity.

All supplementary materials supporting the main article are provided here, including complete study characteristics, inter-coder agreement matrices, quality appraisals, thematic analyses, and detailed appendices with operational definitions and conceptual frameworks.




Supplementary Material
Description




SM-01
Study Characteristics Mapping – Complete bibliographic data, context, and methodology for all 254 studies


SM-02
Inter-Coder Agreement Matrix – Theme-level Fleiss' κ scores demonstrating reliability (mean κ = 0.87)


SM-03
JBI Critical Appraisal – Quality assessment scores with strengths and limitations for each study


SM-04
PICo and Thematic-Content Analysis Matrix – Population-Interest-Context mapping with operational definitions


SM-05
Theme-SWOT-CIMO Analysis – SWOT categorization with dominant mechanisms for all studies


SM-06
Distribution of Reviewed Studies – Geographic, methodological, and educational level distribution (N=254)


SM-07
Condensed Evidence Matrix – PICo × DEAM-MRE Domain × Region × Mechanism (N=254)


SM-08
Mechanism Density Analysis – CIMO framework distribution across the corpus


SM-09
Intercoder Reliability Summary – Derived from SM-02 with domain-level aggregation
DOI: 10.5281/zenodo.18758544
title: Data Set Artificial Intelligence and Multicultural Religious Education: A Systematic Review of Ethical Mediation and Decolonial Practices

description: This dataset comprises the complete supplementary materials for a decolonial qualitative systematic review examining how Artificial Intelligence (AI) mediates Multicultural Religious Education (MRE) across Islamic, Christian, Jewish, Hindu, Buddhist, and indigenous educational traditions. The review synthesizes 254 peer-reviewed studies (2014–2026) from English, Indonesian, and Arabic sources, following PRISMA 2020 guidelines.

The core contribution is an empirically-grounded framework identifying six AI mediation mechanisms along a continuum from algorithmic substitution (moral outsourcing, normative amplification, cultural flattening) to dialogic integration (hybrid deliberative mediation, contextual value re-embedding, dialogic ethical framing). The analysis further documents five decolonial possibilities emerging from Global South practices: epistemic re-centering, pedagogical sovereignty, cultural counter-flattening, governance innovation, and transnational solidarity.

All supplementary materials supporting the main article are provided here, including complete study characteristics, inter-coder agreement matrices, quality appraisals, thematic analyses, and detailed appendices with operational definitions and conceptual frameworks.




Supplementary Material
Description




SM-01
Study Characteristics Mapping – Complete bibliographic data, context, and methodology for all 254 studies


SM-02
Inter-Coder Agreement Matrix – Theme-level Fleiss' κ scores demonstrating reliability (mean κ = 0.87)


SM-03
JBI Critical Appraisal – Quality assessment scores with strengths and limitations for each study


SM-04
PICo and Thematic-Content Analysis Matrix – Population-Interest-Context mapping with operational definitions


SM-05
Theme-SWOT-CIMO Analysis – SWOT categorization with dominant mechanisms for all studies


SM-06
Distribution of Reviewed Studies – Geographic, methodological, and educational level distribution (N=254)


SM-07
Condensed Evidence Matrix – PICo × DEAM-MRE Domain × Region × Mechanism (N=254)


SM-08
Mechanism Density Analysis – CIMO framework distribution across the corpus


SM-09
Intercoder Reliability Summary – Derived from SM-02 with domain-level aggregation
DOI: 10.5281/zenodo.18713582
title: STAMM3D: Dataset and Benchmark for Non-Deformable 3D Image Registration in Multiple Modalities

description: Anonymized repository for submission 614 - MICCAI 2026
DOI: 10.5281/zenodo.18713581
title: STAMM3D: Dataset and Benchmark for Non-Deformable 3D Image Registration in Multiple Modalities

description: Anonymized repository for submission 614 - MICCAI 2026
DOI: 10.15468/dl.kvswhn
title: Occurrence Download

description: A dataset containing 141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Oceania",
 "TaxonKey is Mola mola"
 ]
}

 The dataset includes 141 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007295-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18757404
title: Нормализованный датасет форм ФСН ОО-1, СПО-1, ВПО-1: выпускники и приём по уровням образования в разрезе регионов РФ, 2025
title: Normalized dataset of Russian federal statistical reporting forms OO-1, SPO-1, VPO-1: graduates and enrollment by education level across Russian regions, 2025

description: Датасет сформирован на основе данных форм федерального статистического наблюдения (ФСН): ОО-1 и СПО-1, опубликованных Министерством просвещения РФ (https://edu.gov.ru/activity/statistics/), и ВПО-1, опубликованной Министерством науки и высшего образования РФ (https://www.minobrnauki.gov.ru/action/stat/highed/). Исходные данные представлены в виде разрозненных файлов Excel с нестандартной структурой. Авторами произведены: парсинг файлов, объединение таблиц, нормализация структуры, приведение к единому плоскому формату (tidy data). Датасет описывает численность выпускников 9–11 классов и СПО, а также приём в СПО и ВО по уровню предыдущего образования в разрезе субъектов РФ за 2025 год. Позволяет рассчитать распределение образовательных потоков учащихся по уровням образования. Не учитывает образовательную миграцию (т.е. учитываются регионы обучения, а не регионы постоянного проживания учащихся).

Рекомендуемое цитирование на русском языке:

Для научных статей (по ГОСТ):



Захарова С., Анисимова К. Нормализованный датасет форм ФСН ОО-1, СПО-1, ВПО-1: выпускники и приём по уровням образования в разрезе регионов РФ, 2025. Версия 1.0. [Набор данных]. Zenodo, 2026. URL: https://doi.org/10.5281/zenodo.18757404 (дата обращения: 24.02.2026). Режим доступа: по запросу.


Для аналитических отчетов и презентаций:



Захарова С., Анисимова К. Нормализованный датасет форм ФСН ОО-1, СПО-1, ВПО-1: выпускники и приём в разрезе регионов РФ (2025) // Zenodo. Условия использования: CC BY 4.0. Доступ по запросу: https://doi.org/10.5281/zenodo.18757404
description: The dataset is based on Russian federal statistical reporting (FSR) forms OO-1 and SPO-1 (Ministry of Education of the Russian Federation) and VPO-1 (Ministry of Science and Higher Education of the Russian Federation). Raw data were provided as fragmented Excel files with non-standard structure. Processing included: file parsing, table merging, structure normalization, and conversion to a flat tidy-data format. The dataset covers graduates of grades 9–11 and VET programs, and enrollment in VET and higher education by prior education level, disaggregated by Russian regions, for 2025.
DOI: 10.34847/nkl.afafnqph
title: Chemins anciens de la plaine du Vistre (Nîmes)

description: Jeu de données vectorielles (polylignes) au format .shp cartographiant les chemins anciens de la plaine du Vistre à partir de l'analyse morphologique du cadastre napoléonien de Nîmes (1825) et de quelques observations archéologiques. Il a été réalisé par Magali Watteaux dans le cadre de l'ANR PARCEDES.
Le système de coordonnées projetées utilisé est le RGF Lambert 93 (EPSG:2154).
Cette analyse morphologique des chemins a été réalisée à partir de la vectorisation des plans parcellaires du cadastre napoléonien de Nîmes, également déposée sur Nakala (identifiant : 10.34847/nkl.ece0xu1h).
description: Relation: Données visualisables dans le webSIG du terrain nîmois de l'ANR PARCEDES :
description: Relation: https://parcedes.hypotheses.org/3836
description: Ce jeu de données a été réalisé dans le cadre du programme de recherche PARCEDES (21-CE27-0017-01) financé par l’Agence nationale de la recherche (ANR). Ce programme collectif conduit entre 2022 et 2025 a rassemblé des chercheurs étudiant l’organisation et l’évolution des parcellaires agraires – ou limites de champs – de la Protohistoire à nos jours. Les analyses portent sur quatre terrains situés en France (Vendée et territoire Nîmois), en Italie (Maremma en Toscane) et en Angleterre (South Hams dans le Devon) et impliquent plusieurs institutions et centres de recherche (entre autres, Université Rennes 2, Université de Sienne, Université de Newcastle, INRAP, Conseil départemental de la Vendée).
description: https://parcedes.hypotheses.org/the-project
description: Temporal: 1825
description: Ce dossier zippé contient un jeu de données vectorielles (polylignes) au format .shp cartographiant les chemins anciens de la plaine du Vistre à partir de l'analyse morphologique du cadastre napoléonien de Nîmes (1825) et de quelques observations archéologiques. Il a été réalisé par Magali Watteaux dans le cadre de l'ANR PARCEDES.
Le système de coordonnées projetées utilisé est le RGF Lambert 93 (EPSG:2154).
DOI: 10.5281/zenodo.18757403
title: Нормализованный датасет форм ФСН ОО-1, СПО-1, ВПО-1: выпускники и приём по уровням образования в разрезе регионов РФ, 2025
title: Normalized dataset of Russian federal statistical reporting forms OO-1, SPO-1, VPO-1: graduates and enrollment by education level across Russian regions, 2025

description: Датасет сформирован на основе данных форм федерального статистического наблюдения (ФСН): ОО-1 и СПО-1, опубликованных Министерством просвещения РФ (https://edu.gov.ru/activity/statistics/), и ВПО-1, опубликованной Министерством науки и высшего образования РФ (https://www.minobrnauki.gov.ru/action/stat/highed/). Исходные данные представлены в виде разрозненных файлов Excel с нестандартной структурой. Авторами произведены: парсинг файлов, объединение таблиц, нормализация структуры, приведение к единому плоскому формату (tidy data). Датасет описывает численность выпускников 9–11 классов и СПО, а также приём в СПО и ВО по уровню предыдущего образования в разрезе субъектов РФ за 2025 год. Позволяет рассчитать распределение образовательных потоков учащихся по уровням образования. Не учитывает образовательную миграцию (т.е. учитываются регионы обучения, а не регионы постоянного проживания учащихся).

Рекомендуемое цитирование на русском языке:

Для научных статей (по ГОСТ):



Захарова С., Анисимова К. Нормализованный датасет форм ФСН ОО-1, СПО-1, ВПО-1: выпускники и приём по уровням образования в разрезе регионов РФ, 2025. Версия 1.0. [Набор данных]. Zenodo, 2026. URL: https://doi.org/10.5281/zenodo.18757404 (дата обращения: 24.02.2026). Режим доступа: по запросу.


Для аналитических отчетов и презентаций:



Захарова С., Анисимова К. Нормализованный датасет форм ФСН ОО-1, СПО-1, ВПО-1: выпускники и приём в разрезе регионов РФ (2025) // Zenodo. Условия использования: CC BY 4.0. Доступ по запросу: https://doi.org/10.5281/zenodo.18757404
description: The dataset is based on Russian federal statistical reporting (FSR) forms OO-1 and SPO-1 (Ministry of Education of the Russian Federation) and VPO-1 (Ministry of Science and Higher Education of the Russian Federation). Raw data were provided as fragmented Excel files with non-standard structure. Processing included: file parsing, table merging, structure normalization, and conversion to a flat tidy-data format. The dataset covers graduates of grades 9–11 and VET programs, and enrollment in VET and higher education by prior education level, disaggregated by Russian regions, for 2025.
DOI: 10.5281/zenodo.18759185
title: DATABASE SQL LANGUAGE

description: This article focuses on the importance of choosing the right database management systems (DBMS) in modern software projects. It analyzes popular databases such as MySQL, MariaDB, Oracle, PostgreSQL, MSSQL, SQLite, MongoDB, Redis, Cassandra, Elasticsearch, Firebase and DynamoDB. The article examines the main advantages and disadvantages of relational and Nosql types of DBMS, and highlights aspects that help evaluate the suitability of different technologies and approaches for business and software. Also, the issues of working with big data (Big Data), cloud technologies and data warehouses are covered.
DOI: 10.15468/dl.z877qh
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Megalyra fasciipennis",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018385-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34894/t4e0dk
title: 2028-10-13 - OLOS - CoreTrustSeal Requirements 2020-2022

description: CoreTrustSeal certification
DOI: 10.6084/m9.figshare.30354076.v5
title: Taraba State University, Jalingo 2025/2026 Admission List (1st & 2nd) is Out08053738196) 08053738196 DR Ben Idahosa for Admission Help into Third and Final Batch Call Registrar :08053738196 To check your admission status on the Admission List, Processing Of Admission Into Any Department And Purchasing Of 2025/2026 Last Supplementary Form For New Student Into Any Program, call the School's Admissions Office now on 08053738196.

description: Taraba State University, Jalingo 2025/2026 Admission List (1st & 2nd) is Out08053738196) 08053738196 DR Ben Idahosa for Admission Help into Third and Final Batch Call Registrar :08053738196 To check your admission status on the Admission List, Processing Of Admission Into Any Department And Purchasing Of 2025/2026 Last Supplementary Form For New Student Into Any Program, call the School's Admissions Office now on 08053738196.
DOI: 10.15156/bio/sh1404382.10fu
title: SH1404382.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404384.10fu
title: SH1404384.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404385.10fu
title: SH1404385.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404386.10fu
title: SH1404386.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404387.10fu
title: SH1404387.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404388.10fu
title: SH1404388.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404389.10fu
title: SH1404389.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1404390.10fu
title: SH1404390.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.48544/9e707377-fae5-447a-a117-5c353521fdb8
title: Kunsteducatie, creativiteit en de rol van autonomie