Training institutes
We provide training sessions, webinars and practical workshops for research institutes, laboratories and research groups on research data management, FAIR data, DataverseUA, metadata, README, DMP, OpenAIRE and EOSC-related preparation.
The service helps institutions build internal capacity for data publication, metadata preparation, FAIR assessment and the development of data stewardship practices.
Submit a training request
What this service supports
Institutional training
We organise training sessions for institutes, departments and research groups on practical research data management and FAIR data preparation.
DataverseUA and data publication
We explain how to prepare datasets, metadata, README files and licences for publication in DataverseUA.
Data steward capacity building
We support the development of skills for data stewards, curators, institutional contact persons and researchers involved in data preparation.
EOSC and OpenAIRE awareness
We introduce researchers to OpenAIRE, EOSC-related services, metadata standards, interoperability and European open science practices.
How the process works
1. Request
An institute, department or research group submits a training request and briefly describes the target audience and expected topic.
2. Needs clarification
The Competence Center clarifies the training format, number of participants, level of experience and practical needs.
3. Training programme prepared
The Center proposes a short training programme, webinar, workshop or practical session adapted to the audience.
4. Training delivered
The training session is delivered online or in another agreed format, with practical examples, templates and guidance materials.
5. Follow-up support
Participants may receive links to resources, templates, checklists, recordings or further consultation through the Competence Center services.
What institutions should prepare
To make the training useful, institutions are encouraged to prepare:
- name of the institute, department or research group;
- contact person responsible for coordination;
- expected number of participants;
- target audience: researchers, PhD students, data stewards, curators, librarians, project teams or infrastructure staff;
- preferred training topic;
- level of previous experience with research data management;
- whether the session should be introductory or practical;
- examples of datasets, workflows or data publication cases, if available;
- preferred language and format;
- expected training date or timeframe;
- specific questions that should be addressed during the session.
Expected outcomes
Depending on the request, the outcome may include:
- introductory training on research data management and FAIR principles;
- practical workshop on DataverseUA publication;
- training on metadata preparation and README files;
- training on Data Management Plans;
- training on FAIR assessment of datasets;
- training for data stewards and curators;
- training materials, slides or links to resources;
- recommendations for institutional data management practices;
- identification of datasets or cases for further consultation;
- proposal for Data Café or follow-up support.
Typical training topics include:
1. Introduction to research data management
Basic concepts of research data, data lifecycle, documentation, publication, sharing and preservation.
2. FAIR principles in practice
How to make datasets findable, accessible, interoperable and reusable through metadata, documentation, identifiers and licences.
3. DataverseUA for researchers
How to prepare a dataset for publication in DataverseUA, including metadata, README, licence, file structure and related publications.
4. Metadata preparation How to describe datasets, software, workflows and other research outputs using structured metadata and relevant standards.
5. README and documentation
How to prepare README files, file inventories, manifests and documentation needed for reuse.
6. Data Management Plans
How to prepare a DMP for research projects, datasets or FAIR data publication workflows.
7. FAIR assessment and pre-check
How to assess dataset readiness before publication and identify missing metadata, documentation, licences or access conditions.
8. OpenAIRE and EOSC-related preparation
How repository records, metadata and resource descriptions support visibility, harvesting, cataloguing and interoperability in European open science services.
9. Data stewardship and institutional roles
How institutions can organise support for researchers through data stewards, curators, contact persons and internal data management practices.
Submit a request
To request institutional training, a webinar or a practical workshop, please use the consultation request form and indicate the expected topic and target audience.
Submit a training request