Kyiv Academic University
Short profile
Kyiv Academic University acts as institutional partner for DataverseUA-related data and FAIR support services within the Digital Materials Research Infrastructure. It supports repository-based publication, metadata preparation, user guidance, training and coordination of FAIR data workflows. KAU contributes expertise in training design, dissemination, and competence-centre operations to ensure adoption.
Organisation type: University
Roles: Coordinator, Training provider, FAIR competence provider, Data stewardship provider
Website: https://kau.org.ua/en/about
Related facilities
FAIR Data Competence Center for Digital Materials Research
FAIR Data Competence Center is a digital facility that supports FAIR data packaging, metadata preparation, README and DMP guidance, dataset citation and user training for research groups working …
Центр компетенції даних FAIR для дослідження цифрових матеріалів
Центр компетенції з обробки даних FAIR – це цифровий центр, який підтримує пакування даних FAIR, підготовку метаданих, керівництво README та DMP, цитування наборів даних та навчання користувачів для …
Provided services
FAIR packaging of experimental materials datasets
This service helps users prepare experimental materials datasets for publication, citation and reuse. It focuses on structuring raw and processed experimental data, documenting methods and instruments, preparing README …
Metadata and DOI support for materials data
Metadata and Digital Object Identifier (DOI) support for materials data is a critical aspect of open science, ensuring that datasets are findable, accessible, interoperable, and reusable (FAIR). DataCite, …
README and DMP support for RI users
This service helps RI users prepare README files and data management plan documentation for computational, experimental and mixed materials science datasets.
Training on digital materials workflows
This service provides introductory and practical training on digital materials research workflows, including computational modelling, experimental data preparation, FAIR data documentation and the use of RI resources and …
Reproducible computational workflows with Jupyter / AiiDA
This service supports users in understanding and preparing reproducible computational workflows based on Jupyter notebooks, AiiDA workflow management and related computational materials science tools.
Expert consultation for RI users
This service provides expert consultation for RI users preparing computational, experimental or mixed materials science workflows, datasets and documentation for service use, publication or future onboarding.
Support for pilot use case development
This service helps partners and research groups develop pilot use cases that connect research needs, partners, facilities, resources, services and expected FAIR outputs into demonstrable RI pilot chains.
Support for computational workflow planning
This service supports research groups in planning computational materials science workflows, including task definition, software selection, input preparation, output documentation, provenance capture and FAIR data publication planning.