Research Data Management: practical course

This OSF component hosts the materials used for the transferable skills courses on Research Data Management (RDM) organized by Ghent University Doctoral Schools. This course will help doctoral students to develop their knowledge and practical skills in handling and managing the research data they collect. Having these skills becomes increasingly important to researchers seeking to advance their careers. The lecturer will guide the attendees through the key aspects of how to manage, document, store and safeguard research data well and how to plan and implement good data management in research projects.

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Classification Information

Description: Classification Information

Field Value
Domain Generic
Domain Social Sciences
Domain Humanities
Domain Natural Sciences
Domain Engineering and Technology
Domain Medical and Health Sciences
Availability Information

Description: Availability Information

Field Value
Country Belgium - BEL
Language eng, English
Learning Information

Description: Attributes characterising the Learning resource.

Field Value
Competence Not Available
Duration 8 hours
Learning Outcome(s) Can define Research Data Management (RDM) and can describe its relevance and benefits
Learning Outcome(s) Can explain the steps of the research data lifecycle.
Learning Outcome(s) Can recognize the relationship between FAIR, RDM and Open.
Learning Outcome(s) Can explain what Open is according to the Open Definition.
Learning Outcome(s) Can describe the concept of Open Science and explain its benefits.
Learning Outcome(s) Can list different Open Science practices.
Learning Outcome(s) Is able to identify RDM and OS policies (research funders, publishers, Ghent University policy) that are applicable to the project.
Learning Outcome(s) Understands rights and obligations regarding research outputs set by the Ghent University policy on scholarly publishing.
Learning Outcome(s) Can explain where to get support with regard to Open Science, RDM and the FAIR principles.
Learning Outcome(s) Can describe what a data management plan (DMP) is.
Learning Outcome(s) Can tell which areas should be covered in a DMP.
Learning Outcome(s) Can create a plan and select the appropriate template in DMPonline.be.
Learning Outcome(s) Can detect ethical or legal issues in their project and solve them together with ethical and legal experts (e.g.,ethics committee, data protection officers or TechTransfer).
Learning Outcome(s) Can explain copyright and other IP rights applicable to different research outputs.
Learning Outcome(s) Can explain what personal data is.
Learning Outcome(s) Can describe directly identifying attributes and detect them in data.
Learning Outcome(s) Can identify special categories of personal data.
Learning Outcome(s) Can differentiate between primary and secondary processing of personal data.
Learning Outcome(s) Is able to list the GDPR basic principles relating to processing of personal data.
Learning Outcome(s) Can select the appropriate legal ground to process personal data in their project.
Learning Outcome(s) Can identify data security risks and mitigation measures.
Learning Outcome(s) Can explain general requirements on data protection and access control.
Learning Outcome(s) Can explain different types and functions of storage systems.
Learning Outcome(s) Can identify different options for data storage and their operational aspects.
Learning Outcome(s) Can compare different storage options.
Learning Outcome(s) Can describe what a backup is and tell reasons for backup creation.
Learning Outcome(s) Understands different types of backup (e.g. incremental vs. differential).
Learning Outcome(s) Can explain institutional backup solutions and apply them to own files.
Learning Outcome(s) Can solve backup problems independently or with further assistance from support personnel.
Learning Outcome(s) Can define the concept of encryption.
Learning Outcome(s) Can discern situations for which encryption is recommended or necessary.
Learning Outcome(s) Can identify data formats.
Learning Outcome(s) Can explain the difference between open and proprietary file formats.
Learning Outcome(s) Can select preferred and/or acceptable file formats for data types of interest.
Learning Outcome(s) Understands the importance of file naming conventions and file organization.
Learning Outcome(s) Can explain what version control is and why it is important.
Learning Outcome(s) Can identify version control techniques or tools applicable to the project.
Learning Outcome(s) Can apply best practices for file naming and organization.
Learning Outcome(s) Can paraphrase the FAIR principles.
Learning Outcome(s) Can contrast FAIR and Open.
Learning Outcome(s) Can recognise PIDs and explain different types and use cases for PIDs (e.g. ORCID for researchers, DOI for data, ROR for research organizations, etc.).
Learning Outcome(s) Can explain the importance of PIDs for FAIR data.
Learning Outcome(s) Can explain the importance of PIDs for the dissemination of scholarly outputs.
Learning Outcome(s) Can explain the purpose of the documentation.
Learning Outcome(s) Can identify different types of data documentation.
Learning Outcome(s) Can define metadata and basic related concepts (e.g. structured data, machine readability).
Learning Outcome(s) Can relate metadata to findability (FAIR).
Learning Outcome(s) Can indicate the main differences between generic and domain specific metadata standards.
Learning Outcome(s) Can explain the role of licences in sharing research outputs.
Learning Outcome(s) Can differentiate between different types of licences.
Learning Outcome(s) Can describe the main terms and conditions of standard licences.
Learning Outcome(s) Can appraise the usefulness of metadata standards to describe a resource.
Learning Outcome(s) Can define Open Data.
Learning Outcome(s) Can demonstrate the advantages of Open Data.
Learning Outcome(s) Understands why data should be "as open as possible, as closed as necessary".
Learning Outcome(s) Can identify legitimate factors restricting data sharing.
Learning Outcome(s) Can compare different ways of sharing data and explain their advantages and disadvantages.
Learning Outcome(s) Can explain what a trusted data repository is and how to find it (re3data.org and FAIRsharing).
Learning Outcome(s) Can execute steps in metadata publication.
Learning Outcome(s) Can deposit metadata in a repository.
Learning Outcome(s) Can use a trusted repository to share research output.
Learning Outcome(s) Can apply PIDs to their own research outputs.
Level Basic
Skill Plan and design
Target Data steward
Target Researcher
Target Data librarian or institutional level data steward
Additional Info
Field Value
Access Rights open
Creator Ghent University Data Stewards
Version Date(s) 2022-11-15
system:type Course
Management Info
Field Value
Author Oset Paula
Maintainer Oset Paula
Version 1
Last Updated 29 November 2022, 10:44 (CET)
Created 29 November 2022, 10:42 (CET)