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 services.
What the user gets
Users receive structured training materials, practical examples and guided sessions on how to organise digital materials workflows, connect experimental and computational results, prepare reusable outputs and use the RI service catalogue, resources and pilot chains.
Service category: Training and Consultancy Service
Hosting partner: Kyiv Academic University
Related node / facility: FAIR Data Competence Center for Digital Materials Research
Resources used
- AiiDA
- Jupyter-based workflows
- DMP template for RI users
- Metadata checklist for materials datasets
- Dataset citation and acknowledgement template
- FAIR checklist for materials datasets
Access modes
- Remote access
- Virtual access
- Collaborative access
- Pilot access
Typical data outputs
- Metadata record
- README file
- DMP-related documentation
- FAIR data package
FAIR requirements
Training materials should introduce FAIR principles, basic metadata requirements, documentation practices, repository deposit options and the role of README files, licences, provenance and dataset citation in materials science workflows.
User obligations
Users are expected to participate in training activities, provide feedback on practical needs, test proposed templates or workflows where relevant and apply the recommended documentation and FAIR data practices in their own research context.