Research Data Management in (Bioscience) Engineering and Natural Sciences – a practical training course

Cluster

Research & Valorization

Target Group

Doctoral students of the Doctoral School of (Bioscience) Engineering and Natural Sciences, 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.

Organizing Committee & Lecturers

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

Dates & Venue

  • Tue 16 (PM) + Wed 17 (AM) March 2021

Time schedule

Research Data Management in BSE and NS

Lecturer: Stefanie De Bodt and Paula Oset Garcia 

DAY 1

Program   ONLINE SESSIONS


Tue 16 March (PM) 2021

from 13:00 - 16:30

ONLINE

Part 1
13h00-13h25    Welcome & outline
13h25-14h45    RDM Introduction
•    What is RDM?
•    The research data life cycle
•    RDM policies
Data management planning
•    What is a data management plan?
•    How to write a DMP using DMPonline.be ?
    break
15h00-16h30    Documenting research data
•    Best practices to document your research data
•    Introduction to metadata
Security
•    Safeguarding data from malicious and accidental harm
•    How to use encryption?

Wed 17 March (AM) 2021

from 9:00 - 13:00

ONLINE

Part 2 9h00-11h00    Storing your research data
•    Overview of UGent infrastructure to store & back-up research data Organization
•    Best practices for file formats, file naming and folder structure
•    Version control
    break
11h15-13h00    FAIR data
•    The FAIR principles
•    Data sharing, repositories & licensing
•    Open Science
Privacy
•    Collecting personal data
•    The GDPR
13h00-13h05    Outro

Registration fee

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

Registration

If the course is fully booked, you can ask to be added to the waiting list by sending an email to doctoralschools@ugent.be Please mention your student ID nr.

Please read the cancellation policy: cancellationpolicycourses

Teaching and learning material

Lecture combined with practical exercises. Presentation slides.

Number of participants

maximum 45

Language

English

Evaluation methods and criteria (doctoral training programme)

100 % participation