Research Data Management 101

Course Description

The RDM 101 course provides you with the essential knowledge and the core skills to manage research data (and other relevant research objects) according to best practice. The application of this knowledge to your research will allow you to reflect on how to work efficiently and in a reproducible manner with your research data, while complying with funders and institutional requirements.

Target Audience

This course is aimed at PhD candidates in their first year who require a hands-on introduction to Research Data Management (RDM) and Data Management Plans (DMPs).

Learning Objectives

After this course, learners:

• realise the important role that good data management plays in research
• identify different types of research data and recognize the regulations, policies and/or legal requirements associated with them
• list the main components of the FAIR data principles and connect them to your own research workflows
• employ the acquired knowledge to design an efficient research data management strategy for your projects according to best practices

Course setup

This is a three-week blended course, which consists of a combination of face-to-face or online class meetings and self-study modules. Additionally, learners have to complete one assignment per week.

All class meetings are mandatory and follow a coaching approach that fosters collaborative work and creates a task-based learning environment. Participants and trainers meet in one class (face-to-face or online) each week.

The self-study part of the course consists of five modules. These are delivered through Brightspace. Each module contains self-study elements such as lessons (in video and/or text format), discussion forums where participants share their findings and experiences with other participants and receive feedback from the trainers, and interactive quizzes.

The estimated total workload of the course is 22 hours, which is equivalent to 2 GS credits in the Research Skills category of the GS Education programme, if the requirements are met.

Course Programme

Structure of the Self-paced Online Part:

• Module 0: Getting started (15 minutes)
• Module 1: Awareness about Research Data Management (RDM) (30 minutes)
• Module 2: Essentials for RDM (2 hours and 45 minutes)
• Module 3: FAIR Data Principles and Their Key Elements (2 hours)
• Module 4:  Realising FAIR Data (1 hour and 30 minutes)
• Module 5:  How to Plan for RDM (2 hours and 30 minutes)
• Module 6: Final steps (5 min)

Structure of the In-Person or Online Class Sessions

• Class 1: Getting to know each other, introduction to the course and data types (2 hours)
• Class 2: Exploring tools for documentation and FAIR data (2 hours)
• Class 3: Wrap up (2 hours)

Assignments

• Week 1. Data flow map - part 1 (estimated time: 1 hour and 30 minutes plus 1 hour peer-review)
• Week 2. Data flow map - part 2 (estimated time: 2 hours and 30 minutes)
• Week 3: Reflection on your RDM Strategy (estimated time: 1 hour and 25 minutes)

Prerequisites

No prior knowledge is needed to take this course. This is an introductory course particularly useful for first year PhD candidates.

If you are a 3rd and 4th year PhD candidate and you are interested in learning about RDM, we suggest you to learn about it through the self-learning resource: https://tu-delft-library.github.io/rdm101-book/intro.html (without provision of GS credits).

Registration

The registration to the course for PhD candidates is via Coachview, the course registration application of the Graduate School Doctoral Education (GS DE) programme.