Research Data Management

This course for PhD candidates and postdocs is offered by Wageningen Graduate Schools and organised by WUR Library. It consists of lectures and practical assignments that cover various aspects of managing research data: from organising your data files during data collection to publishing your final data set.

Course outline

The course structure follows the order of data management topics in the WUR data management plan template. This structure allows participants to fill in or update their data management plan during the course.

  • Part 1 focuses on what data management is, deals with data storage options and the support and services available at WUR.
  • Part 2 deals with how to keep your data organised, how to document your data and why to share it.
  • Part 3 goes into how to prepare your data for the future, where and how to look for suitable repositories and data licences.  

You are obliged to take all three parts.

General Information
Audience PhD's and postdocs
Maximum group size 25
Course duration 3 hours each part, 9 hours in total
Language English
Credit points 0.45 ECTS
Self study 1 hour each part, 3 hours in total
Lecturers Irene Verhagen (course leader) and Shauna NĂ­ Fhlaithearta
Venue online

Programme

Part 1: What is data management and where to store your data. At the end of part 1 you will:

  • be able to define research data and research data management
  • be familiar with the data management requirements, support and services at WURbe familiar with the concept of FAIR data
  • be introduced to data management plans
  • be familiar with the data storage solutions at WUR
  • understand the (dis)advantages of different storage options;

Part 2: How to set up your data collection system, how to document your data and why share it? At the end of part 2 you will:

  • understand how to systematically organise folders and (versions of) files
  • know how to keep research notes to make a data set understandable
  • understand the importance of making research data available to others
  • know how to prepare a data set for submission to a repository;

    Part 3: How to make your data future-proof. At the end of part 3 you will:

    • be aware of issues concerning privacy when working with people
    • how to select and prepare your data for the future
    • know where and how to look for suitable repositories for your data
    • be familiar with different open data licenses and their implications.