15 - Training Researchers and Data Literacy

Summary

Aims

The aim of this chapter is to work through the decisions that need to be made to set up a programme of training about research data management.

Introduction

Central to any RDS will be the function of training members of the organisation in RDM. The chapter is organised around a series of interrelated decisions involved in designing such a training programme.

One of the outcomes of your talking to researchers and your more systematic user requirements gathering (Chapter 9) will be to have a feel for what the needs are around the institution. But determining audiences, content and channels for training is in itself quite challenging.

Exploring further

Think about some of the more engaging CPD or training courses you have been on. What made the best events stand out? It could be:

• the immediate usefulness of what you learned

• the hands-on time

• what you learned from other people on the course, as opposed to the trainers

• the time you were given to talk through how the challenge affected you

• the warmth and helpfulness of the trainers

• something else.

Think about how you can manage the learning situation to recreate such a good learning experience for anyone who comes on one of your courses.

Step 1: Who is the training for?

At some level it may be that every researcher in the institution needs some training in basic awareness of institutional policy and guidelines and to draw attention to critical areas of risk. But attempting to develop training that feels relevant to everyone, in every discipline and level of experience, is challenging.

It is probable that people in different meta-disciplines will have widely differing views on what RDM is about. That does not mean that they should be taught separately but it will take extra effort to ensure that the material feels relevant to everyone. An obvious compromise is to organise training by meta-discipline (e.g. for humanities scholars separate from engineers). Training customised to each department or subject discipline may not be scalable.

Actually it could be argued that those with similar methodologies might be best brought together, regardless of their field.