Cluster

Research & Valorization

Target Group

Doctoral students of all Doctoral Schools, no foreknowledge required.

Abstract

This in-depth 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.

Learning outcomes of the course

Upon completion of this course, students should have an understanding of what Research Data Management is, what it all comprises, and why it is important in academic research. They should have an understanding of the FAIR data principles, and how they can make data more FAIR. They should be able to successfully manage all types of research data and to document both the research itself, as well as the data in a comprehensive way.

Students should be able to comply to the UGent and funders’ policies with regard to RDM- and DMP (Data Management Plan) requirements. They should also be fully aware how to use UGent infrastructure for RDM related tasks, and able to work with data in a secure way (both in terms of physical storage as in methods to safeguard sensitive/personal data).

Topic of the course

Essential key-concepts and skills in Research Data Management(RDM) will tackled. This hands-on workshop will focus on all kinds of data (both qualitative and quantitative) and cover the following aspects:

• Introduction: Why and how to manage research data?
• What is FAIR data? (Findable, Accessible, Interoperable, Reusable)
• Planning: How to plan your research data management and write a data management plan?
• Documenting: How to make research data and data processing understandable and reusable?
• Storage: Strategies for storing data during and after the project.
• Security: How to safeguard your data?
• Organisation & structure: Strategies for naming, organising and structuring your data files.
• Data Sharing & Open Science: How to share research data? Introduction to open science.
• Ethical and legal issues in data sharing and handling confidential information.
• Working with Personal Data

Organizing Committee & Lecturers

Thomas Van de Velde, Myriam Mertens, Laura Standaert, Paula Oset, Stefanie De Bodt (Data Stewards @ Boekentoren - DOZA)

Time schedule & Venue

Registration fee

Free of charge for Doctoral School members. The no show policy applies.

Registration

Members of the Doctoral School Arts, Humanities and Law, follow this link.

Members of the Doctoral School Social and Behavioural Sciences, follow this link.

Members of the Doctoral School (Bioscience) Engineering, follow this link.

Members of the Doctoral School Life Sciences and Medicine, follow this link.

Members of the Doctoral School Natural Sciences, follow this link.

Teaching and learning material

Lecture combined with practical exercises. Presentation slides.

Number of participants

maximum 25 per Doctoral School

Language

English

Evaluation methods and criteria (doctoral training programme)

100 % participation